Graph outcome determination in domain-specific execution environment

ABSTRACT

A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent is a continuation of U.S. Non-Provisional patent applicationSer. No. 16/893,318, filed 4 Jun. 2020, titled GRAPH OUTCOMEDETERMINATION IN DOMAIN-SPECIFIC EXECUTION ENVIRONMENT, which claims thebenefit of U.S. Provisional Patent Application 62/897,240, filed 6 Sep.2019, titled “SMART DEONTIC DATA SYSTEMS.” This patent also claims thebenefit of U.S. Provisional Patent Application 62/959,377, filed 10 Jan.2020, titled “SMART DEONTIC MODEL AND SYSTEMS.” This patent also claimsthe benefit of U.S. Provisional Patent Application 62/959,418, filed 10Jan. 2020, titled “GRAPH-MANIPULATION BASED DOMAIN-SPECIFICENVIRONMENT.” This patent also claims the benefit of U.S. ProvisionalPatent Application 62/959,481, filed 10 Jan. 2020, titled “GRAPH OUTCOMEDETERMINATION IN DOMAIN-SPECIFIC EXECUTION ENVIRONMENT.” This patentalso claims the benefit of U.S. Provisional Patent Application63/020,808, filed 6 May 2020, titled “COUNTERPARTY SCENARIO MODELING.”This patent also claims the benefit of U.S. Provisional PatentApplication 63/033,063, filed 1 Jun. 2020, titled “MODIFICATION OFIN-EXECUTION SMART CONTRACT PROGRAMS.” This patent also claims thebenefit of U.S. provisional patent application 63/034,255, filed 3 Jun.2020, titled “SEMANTIC CONTRACT MAPS.” The entirety content of eachaforementioned patent filing is hereby incorporated by reference.

BACKGROUND 1. Field

This disclosure relates generally to computer systems and, moreparticularly, to graph-manipulation based domain-specific executionenvironments.

2. Background

It is often useful to specify relationships between agents that definehow one agent will behave with respect to the other, and in some cases,these behaviors are contingent on whether future events occur. Forinstance, in the design of application program interfaces, often the APIspecification is characterized as a contract between the API providerand the API consumer. In other examples, protocol states between variousnon-computer entities, like humans or organizations thereof, may specifyhow different groups commit to behave with respect to one another.

SUMMARY

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

Some aspects include a process that includes obtaining, with a computersystem, a set of conditional statements, wherein a conditional statementof the set of conditional statements is associated with an outcomesubroutine that specifies operations in each of one or more branches ofthe conditional statement, a set of index values index the set ofconditional statements, and a first outcome subroutine of a firstconditional statement of the set of conditional statements uses a firstindex value of the set of index values, wherein the first index value isassociated with a second conditional statement of the set of conditionalstatements. The process may also include executing, with the computersystem, a program instance of an application based on the set ofconditional statements, wherein program state data of the programinstance comprises a set of vertices and a set of directed graph edges,wherein each of the set of vertices comprises a identifier value and isassociated with one of the set of conditional statements, and whereineach of the set of directed graph edges associates a pair of the set ofvertices and a direction from a tail vertex of the pair to a head vertexof the pair, a set of statuses, wherein each of the set of statuses isassociated with one of the set of vertices, a set of vertex categories,wherein each of the set of vertex categories is a category value and isassociated with a respective vertex of the set of vertices and isdetermined based a respective conditional statement of the respectivevertex, and a set of scores, wherein each respective score of the set ofscores is associated with a respective vertex and is based a respectiveconditional statement of the respective vertex. The process may alsoinclude updating, with the computer system, the program state data basedon a set of inputs comprising a first input, wherein updating theprogram state data comprises modifying a status of a first vertex of theset of vertices based on the first input, updating a vertex adjacent tothe first vertex. The process may also include determining, with thecomputer system, an outcome score based on the set of scores afterupdating the program state data.

Some aspects include a tangible, non-transitory, machine-readable mediumstoring instructions that when executed by a data processing apparatuscause the data processing apparatus to perform operations including theabove-mentioned process.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to effectuate operations of the above-mentioned process.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1 is a flowchart of an example of a process by which program statedata of a program may be deserialized into a directed graph, updatedbased on an event, and re-serialized, in accordance with someembodiments of the present techniques.

FIG. 2 depicts a data model of program state data, in accordance withsome embodiments of the present techniques.

FIG. 3 is flowchart of an example of a process by which a program maysimulate outcomes or outcome scores of symbolic AI models, in accordancewith some embodiments of the present techniques.

FIG. 4 show a computer system for operating one or more symbolic AImodels, in accordance with some embodiments of the present techniques.

FIG. 5 includes a set of directed graphs representing triggered normsand their consequent norms, in accordance with some embodiments of thepresent techniques.

FIG. 6 includes a set of directed graphs representing possiblecancelling relationships and possible permissive relationships betweennorms, in accordance with some embodiments of the present techniques.

FIG. 7 includes a set of directed graphs representing a set of possibleoutcome states based on events corresponding to the satisfaction orfailure of a set of obligations norms, in accordance with someembodiments of the present techniques.

FIG. 8 includes a set of directed graphs representing a set of possibleoutcome states after a condition of a second obligations norm of a setof obligations norms is not satisfied, in accordance with someembodiments of the present techniques.

FIG. 9 includes a set of directed graphs representing a set of possibleoutcome states after a condition of a third obligations norm of a set ofobligations norms is not satisfied, in accordance with some embodimentsof the present techniques.

FIG. 10 includes a set of directed graphs representing a pair ofpossible outcome states after a condition of a fourth obligations normof a set of obligations norms is not satisfied, in accordance with someembodiments of the present techniques.

FIG. 11 is a block diagram illustrating an example of a tamper-evidentdata store that may used to render program state tamper-evident andperform the operations in this disclosure, in accordance with someembodiments of the present techniques.

FIG. 12 depicts an example logical and physical architecture of anexample of a decentralized computing platform in which a data store ofor process of this disclosure may be implemented, in accordance withsome embodiments of the present techniques.

FIG. 13 shows an example of a computer system by which the presenttechniques may be implemented in accordance with some embodiments.

FIG. 14 is a flowchart of an example of a process by which a program mayuse an intelligent agent to determine outcome program states for smartcontract programs, in accordance with some embodiments of the presenttechniques.

FIG. 15 is a flowchart of an example of a process by which a program maytrain or otherwise prepare an intelligent agent to determine outcomeprogram states, in accordance with some embodiments of the presenttechniques.

FIG. 16 is a flowchart of an example of a process by which a program maydetermine an inter-entity score quantifying a relationship between apair of entities across multiple smart contract programs, in accordancewith some embodiments of the present techniques.

FIG. 17 depicts a set of directed graphs representing possible outcomeprogram states after a triggering condition of a prohibition norm issatisfied, in accordance with some embodiments of the presenttechniques.

FIG. 18 depicts a directed graph representing multiple possible programstates of a smart contract, in accordance with some embodiments of thepresent techniques.

FIG. 19 depicts a tree diagram representing a set of related smartcontract programs, in accordance with some embodiments of the presenttechniques.

FIG. 20 depicts an example representation of an amendment requestmodifying a directed graph of a smart contract program, in accordancewith some embodiments of the present techniques.

FIG. 21 is a flowchart of a process to modify a program state based onan amendment request, in accordance with some embodiments of the presenttechniques.

FIG. 22 depicts a diagram of an entity graph, in accordance with someembodiments of the present techniques.

FIG. 23 is a flowchart of a process to assign an outcome score based ona graph portion, in accordance with some embodiments of the presenttechniques.

FIG. 24 is a flowchart of a process to send a message indicating that anentity score has been updated based on an entity graph, in accordancewith some embodiments of the present techniques.

FIG. 25 depicts a diagram of a hybrid decentralized computing systemusable for separating application execution logic from query logic, inaccordance with some embodiments of the present techniques.

FIG. 26 depicts a flowchart of a process to retrieve a response valuefrom a hybrid decentralized computing system based on a message, inaccordance with some embodiments of the present techniques.

While the present techniques are susceptible to various modificationsand alternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit thepresent techniques to the particular form disclosed, but to thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the presenttechniques as defined by the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the field oflanguage processing. Indeed, the inventors wish to emphasize thedifficulty of recognizing those problems that are nascent and willbecome much more apparent in the future should trends in industrycontinue as the inventors expect. Further, because multiple problems areaddressed, it should be understood that some embodiments areproblem-specific, and not all embodiments address every problem withtraditional systems described herein or provide every benefit describedherein. That said, improvements that solve various permutations of theseproblems are described below.

Technology-based self-executing protocols, such as smart contracts andother programs, allow devices, sensors, and program code have seenincreased use in recent years. However, some smart contracts andcontract information models often rely on program instructions orindustry-specific data structures, which may be difficult to generalize,use for comparison analysis, or re-use in similar contexts due to minordifferences in contract details. As a result, uses of smart contractshas not extended into areas that are often the domain of naturallanguage documents. Described herein is a process and related system toconstruct, interpret, enforce, analyze, and re-use terms for a smartcontract in a systematic and unambiguous way across a broad range ofapplicable fields. In contrast, contracts encoded in natural languagetext often rely on social, financial, and judicial systems to providethe resources and mechanisms to construct, interpret, and enforce termsin the contracts. As contract terms increase in number or a situationwithin which the contract was formed evolves, such a reliance may leadto a lack of enforcement, ambiguity, and wasted resources spent on there-interpretation or enforcement of contract terms.

Some embodiments may include smart contracts (or other programs) thatinclude or are otherwise associated with a directed graph representing astate of the smart contract. In some embodiments, vertices of the graphmay be associated with (e.g., encode, or otherwise represent) norms(e.g., as norm objects described below) of the smart contract, likeformal language statements with a truth condition paired with aconditional statement that branches program flow (and changes normstate) responsive to the whether the truth condition is satisfied, forinstance, “return a null response if and only if an API request includesa reserved character in a data field.” In some embodiments, norms of asmart contract may represent terms of a contract being represented bythe smart contract, legal conditions of the contract, or otherverifiable statements. As used herein, a smart contract may be aself-executing protocol executable as an application or portion of anapplication on a distributed computing platform, centralized computingsystem, or single computing device. Furthermore, as used herein, a graphmay be referred to as a same graph after the graph is manipulated. Forexample, if a graph being referred to as a “first graph” is representedby the serialized array [[1,2], [2,3], [3,4]] is modified to include theextra vertex and graph edge “[1,5]” and become the modified graphrepresented by the serialized array “[[1,2], [2,3], [3,4], [1,5]],” theterm “first graph” may be used to refer to the modified graph.

A self-executing protocol may be a program, like a smart contract.Self-executing protocols may execute responsive to external events,which may include outputs of third-party programs, and human input via auser interface. A self-executing protocol may execute on a computingsubstrate that involves human intervention to operate, like turning on acomputer and launching an event listener.

A norm of a smart contract may be encoded in various formal languages(like programming languages, such as data structures encoding statementsin a domain-specific programming language) and may include or otherwisebe associated with one or more conditional statements, a set of normconditions of the one or more conditional statements, a set of outcomesubroutines of the one or more conditional statements, a norm status,and a set of consequent norms. In some embodiments, satisfying a normcondition may change a norm status and lead to the creation oractivation of the consequent norms based on the actions performed by thesystem when executing the outcome subroutines corresponding to thesatisfied norm condition. In some embodiments, a norm may be triggered(i.e. “activated”) when an associated norm condition is satisfied by anevent, as further described below. Alternatively, some types of normsmay be triggered when a norm condition is not satisfied before acondition expiration threshold is satisfied. As used herein, atriggerable norm (i.e. “active norm”) is a norm having associated normconditions may be satisfied by an event. In contrast, a norm that is setas not triggerable (i.e. “inactive”) is a norm is not updated even ifits corresponding norm conditions are satisfied. As used herein,deactivating a norm may include setting the norm to not be triggerable.

A smart contract and its norms may incorporate elements of a deonticlogic model. A deontic logic model may include a categorization of eachof the norms into one of a set of deontic primitive logical categories.A deontic primitive logical category (“logical category”) may include alabel such as “right,” “obligation,” or “prohibition.” The logicalcategory may indicate a behavior of the norm when the norm is triggered.In addition, a norm of the smart contract may have an associated normstatus such as “true,” “false,” or “unrealized,” where an event maytrigger a triggerable norm by satisfying a norm condition (and thus“realizing” the norm). These events may be collected into a knowledgelist. The knowledge list may include an associative array of norms,their associated states, an initial norm status during the initialinstantiation of the associated smart contract, their norm observationtimes (e.g., when a norm status was changed, when an event message wasreceived, or the like), or other information associated with the norms.The smart contract may also include a set of consequent actions, where aconsequent action may include an association between a triggered normand any respective consequent norms of the smart contract. As furtherdiscussed below, the set of consequent actions may be updated as eventsoccur and the smart contract state is updated, which may result in theformation of a history of previous consequent actions. It should beunderstood that the term “norm” is used for illustrative purposes andthat this term may have different names in other references andcontexts. The labeling of norms may also be used for symbolic artificialintelligence (AI) systems. As described further below, the use of thesesymbolic AI systems in the context of a smart contract may allow forsophisticated verification and predictive techniques that may beimpractical for pure neural network systems which do not use symbolic AIsystems. It should be understood that, while the term “logical category”is used in some embodiments, other terms may be used for categories ortypes of categories without loss of generality. For example, someembodiments may refer to the use of a “category label” instead of alogical category.

Some embodiments may store a portion of the smart contract state in adata serialization format (“serialized smart contract state data”). Forexample, as further described below, some embodiments may store avertices of a directed graph (or both vertices and edges) in a dataserialization format. In response to determining that an event hasoccurred, some embodiments may deserialize the serialized smart contractstate data into a deserialized directed graph. In some embodiments, avertex (a term used interchangeably with the term node) of the directedgraph may be associated with a norm from a set of norms of the smartcontract and is described herein as a “norm vertex,” among other terms,where a norm vertex may be connected one or more other norm vertices viagraph edges of the directed graph. Some embodiments may then update thedirected graph based on a set of consequent norms and their associatedconsequent norm vertices, where each of the consequent norms aredetermined based on which norms were triggered by the event and whatnorm conditions are associated with those active norms. The updateddirected graph may then be reserialized to update the smart contract. Insome embodiments, a norm vertex may not have any associated conditions.In some embodiments, the amount of memory used to store the serializedsmart contract state data may be significantly less than the memory usedby deserialized smart contract state data. During or after the operationto update the smart contract, some embodiments may send a message toentities listed in a list of entities (such as an associative array ofentities) to inform the entities that the smart contract has beenupdated, where the smart contract includes or is otherwise associatedwith the list of entities. Furthermore, it should be understood in thisdisclosure that a vertex may include (or comprise) a condition by beingassociated with the condition. For example, a norm vertex may include afirst norm condition by including a reference pointer to the first normcondition.

In some embodiments, generating the smart contract may include using anintegrated development environment (IDE) and may include importinglibraries of provisions re-used across agreements. Furthermore, someembodiments may generate a smart contract based on the use of naturallanguage processing (NLP), as further described below. For example, someembodiments may apply NLP operations to convert an existing prosedocument into a smart contract using operations similar to thosedescribed for patent application 63/034,255, titled “Semantic ContractMaps,” which is herein incorporated by reference. For example, someembodiments may apply a set of linear combinations of featureobservations and cross observations across first order and second ordersin feature space to determine a smart contract program or other symbolicAI program. Alternatively, or in addition, some embodiments may includeconstructing a smart contract from a user interface or text editorwithout using an existing prose document. In some embodiments, the smartcontract may be encoded in various forms, such as source code, bytecode,or machine code encodings. In some embodiments, a smart code may begenerated or modified in one type of encoding and be converted toanother type of encoding before the smart code is used. For example, asmart contract may be edited in a source code encoding, and the smartcontract may be executed by converting the smart contract into abytecode encoding executing on a distributed computing platform. As usedherein, a smart contract may be referred to as a same smart contractbetween different encodings of the smart contract. For example, a smartcontract may be written in source code and then converted to a machinecode encoding, may be referred to as a same smart contract.

Furthermore, as used herein, the sets of items of a smart contract datamodel may be encoded in various formats. A set of items be encoded in anassociative array, a b-tree, a R-tree, a stack, or various other typesof data structures. As used herein, the sets of items in the data modelmay be determined based on their relationships with each other. Forexample, a set of entities may be encoded as an associative array ofentities or may be encoded as an entities b-tree, and elements of aknowledge list may include references to an entity in the set ofentities for either type of encoding. In some embodiments, sets of itemsin their respective data models may be based on the underlyingrelationships and references between the items in the sets of items, andembodiments should not be construed as limited to specific encodingformats. For example, while some embodiments may refer to an associativearray of norms, it should be understood that other embodiments may use ab-tree to represent some or all of the set of norms.

A smart contract may be stored on different levels of a memoryhierarchy. A memory hierarchy of may include (in order of fastest toslowest with respect to memory access speed) processor registers, Level0 micro operations cache, Level 1 instructions cache, Level 2 sharedcache, Level 3 shared cache, Level 4 shared cache, random access memory(RAM), a persistent flash memory, hard drives, and magnetic tapes. Forexample, a Level 1 cache of a computing device may be faster than a RAMof the computing device, which in turn may be faster than a persistentflash memory of the computing device. In some embodiments, the memory ofa computing device at a first layer of the memory hierarchy may have alower memory capacity than a memory of the computing device at a slowerlayer of the memory hierarchy. For example, a Level 0 cache may memorycapacity of 6 kibibytes (KiB), whereas a Level 4 cache may have a memorycapacity of 128 mebibytes (MiB). In some embodiments, memory may befurther distinguished between persistent storage and non-persistentstorage (i.e. “non-persistent memory”), where persistent storage iscomputer memory that may retain the values stored in it without anactive power source. For example, persistent storage may includepersistent flash memory, hard drives, or magnetic tape, andnon-persistent memory may include processor registers, cache memory, ordynamic RAM. In some embodiments, a smart contract may be stored onmemory at different levels of the memory hierarchy to increase storageefficiency of the smart contract. For example, serialized smart contractstate data of the smart contract may be stored on RAM of a computingdevice while the deserialized smart contract state data may be stored ona cache of the computing device.

In some embodiments, the smart contract may update infrequently, such asless than once per hour, less than once day, less than once per month,or the like. The relative infrequency of the updates can mean that therelative computing resources required to deserialize and reserializedata be significantly less than the computing resources required tomaintain deserialized data in higher-speed memory. In some embodiments,the dynamic program state By serializing a portion of the smart contractdata and persisting the serialized data instead of the correspondingdeserialized data to a persistent storage, a computing system may usereduce the memory requirements of storing and executing the smartcontract. In addition, the computing system may also increase the numberof smart contracts being executed concurrently by a distributedcomputing platform or single computing device. Furthermore, as usedherein, updating a value may include changing the value or generatingthe value.

As described herein, some embodiments may store smart contract data inother forms. For example, while some embodiments may temporarily store adirected graph in non-persistent storage, some embodiments may store thedirected graph on a persistent storage. In some embodiments, variousother types of information such as norm statuses (e.g. “triggered,”“failed,” “satisfied,” etc.) or logical categories (e.g. “rights,”“obligation,” “prohibition,” etc.) may be included in or otherwiseassociated with some or all of the vertices of the directed graph.Furthermore, some embodiments may generate visual display representingof the program state data to show the directed graph and its associatedstatuses, categories, or other information. For example, as furtherdescribed below, some embodiments may display the directed graph as ahierarchical visual element such as a hierarchy tree in a webapplication.

A smart contract may be implemented in various ways. For example, someembodiments may construct, enforce, or terminate the smart contractusing a distributed ledger or distributed computing system.Alternatively, some embodiments may implement the smart contract using arequest-response system over a public or private internet protocol (IP)network. Use of the methods described herein may increase the efficiencyof smart contract enforcement by advancing the state of complexmulti-entity agreements in a fast and unambiguous way. Furthermore,implementing and using smart contracts with the embodiments describedherein may allow for the comparison, quantification, and reuse of smartcontracts in a way that would be inapplicable to custom-coded smartcontracts.

In some embodiments, the smart contract may be stored in atamper-evident data-store. As discussed below, tamper-evident datastores (e.g., repositories rendering data tamper-evident with one ormore tamper-evident data structures) afford desirable properties,including making it relatively easy to detect tampering with entries inthe data store and making it relatively difficult or impossible totailor entries to avoid such detection. Furthermore, various smartcontracts may be operating across one or more nodes of thetamper-evident data store, reducing the susceptibility of the smartcontract to regional disturbances.

None of the preceding should be taken to suggest that any technique isdisclaimed or that the approaches described herein may not be used inconjunction with other approaches having these or other describeddisadvantages, for instance, some embodiments may use a custom-writtensmart-contract that includes one or more of the norms, data structures,or graphs described herein. Or some embodiments may store a directedgraph without serialization or deserialization operations. Or someembodiments may be implemented on a centralized server without storingsmart contract state data on a distributed computing system such as adecentralized computing system. Further, it should be emphasized thatthe data structures, concepts, and instructions described herein maybear labels different from those applied here in program code, e.g., adata structure need not be labeled as a “node” or a “graph” in programcode to qualify as such, provided that the essential characteristics ofsuch items are embodied.

In some embodiments, the process 100 of FIG. 1, like the other processesand functionality described herein, may be implemented as computer codestored on a tangible, non-transitory, machine-readable medium, such thatwhen instructions of the code are executed by one or more processors,the described functionality may be effectuated. Instructions may bedistributed on multiple physical instances of memory, e.g., in differentcomputing devices, or in a single device or a single physical instanceof memory (e.g., non-persistent memory or persistent storage), allconsistent with use of the singular term “medium.” In some embodiments,the operations may be executed in a different order from that described,some operations may be executed multiple times per instance of theprocess's execution, some operations may be omitted, additionaloperations may be added, some operations may be executed concurrentlyand other operations may be executed serially, none of which is tosuggest that any other feature described herein is not also amenable tovariation.

FIG. 1 is a flowchart of an example of a process by which program statedata of a program may be deserialized into a directed graph, updatedbased on an event, and re-serialized, in accordance with someembodiments of the present techniques. In some embodiments, the process100, like the other processes and functionality described herein, may beimplemented by a system that includes computer code stored on atangible, non-transitory, machine-readable medium, such that wheninstructions of the code are executed by one or more processors, thedescribed functionality may be effectuated. Instructions may bedistributed on multiple physical instances of memory, e.g., in differentcomputing devices, or in a single device or a single physical instanceof memory, all consistent with use of the singular term “medium.” Insome embodiments, the operations may be executed in a different orderfrom that described. For example, while the process 100 is described asperforming the operations of block 112 before block 124, the operationsof block 124 may be performed before the operations of block 112. Someoperations may be executed multiple times per instance of the process'sexecution, some operations may be omitted, additional operations may beadded, some operations may be executed concurrently and other operationsmay be executed serially, none of which is to suggest that any otherfeature described herein is not also amenable to variation.

In some embodiments, the process 100 includes determining that an eventhas occurred based on an event message, a calculation, or a conditionexpiration threshold, as indicated by block 104. In some embodiments,the system may determine that an event has occurred after receiving anevent message at an API of the system indicating that the event hasoccurred. As used herein, an event message may be transmitted across ormore packets over a wired or wireless connection, where a system maycontinuously, periodically, or be activated to listen for an eventmessage. In some embodiments, as described further below, an eventmessage may be transmitted over a public or private IP network.Alternatively, or in addition, the event message may be transmitted viathe channels of a distributed computing system. For example, the eventmessage may be transmitted from a first node of a distributed computingsystem (e.g., a blockchain platform) to a second node of the distributedcomputing system, where the first node and second node may be atdifferent geographic locations (e.g., different nodes executing ondifferent computing devices) or share a same geographic location (e.g.,different nodes executing on a same computing device). Furthermore, anevent message may be sent by a first smart contract executing on a firstcomputing distributed platform to a second smart contract executing on asame or different distributed computing platform. In some embodiments,determining than event has occurred does not require verification thatthe event has occurred. For example, in some embodiments, receiving anevent message indicating an event has occurred may be sufficient for thesystem to determine that the event occurred. Furthermore, in someembodiments, a norm vertex may be triggered based on an event satisfyinga subset of its associated norm conditions. Alternatively, a norm vertexmay be triggered only after an event satisfies all of its associatednorm conditions.

In some embodiments, the event may include satisfying a conditionexpiration threshold associated with a triggerable norm vertex (herein“triggerable vertex”) without satisfying a norm condition associatedwith the triggerable vertex, where a norm condition may be various typesof conditions implemented in a computer-readable form to return a value(e.g., “True,” “False,” set of multiple binary values, or the like). Forexample, a norm condition may include an “if” statement to test whethera payload containing a set of values was delivered to an API of thesystem by a specific date, where a condition expiration threshold isassociated with the norm condition. After the specific date is reached,the system may determine that the condition expiration threshold issatisfied and determine whether the associated norm condition issatisfied. In response to a determination that the norm condition is notsatisfied, the system may determine that an event has occurred, wherethe event indicates that a condition expiration threshold associatedwith a triggered norm vertex (herein “triggered vertex”) is satisfiedand that an associated norm condition of the triggered vertex is notsatisfied. As further stated below, such an event may trigger theassociated norm vertex and result in the activation of a set of norms,where the activation of the set of norms may be represented by thegeneration or association of an adjacent vertex to the triggered vertex,where the adjacent vertex may be updated to be triggerable. As used inthis disclosure, it should be understood that satisfying the conditionexpiration threshold of a triggerable vertex does satisfy a conditionassociated with the triggerable vertex.

In some embodiments, the event message may include a publisheridentifier to characterize a publisher of the event message. As usedherein, a publisher may be an entity and may include various sources ofan event message. For example, a publisher may include a publisher in apublisher-subscriber messaging model or a sender of a response orrequest in a response-request messaging model. In some embodiments, thepublisher identifier may be an entity identifier that is a specific nameunique to a source of the event message. For example, a publisheridentified by the publisher identifier “BLMBRG” may be transmitted inthe event message, where “BLMBRG” is unique to a single publisher.Alternatively, or in addition, a publisher identifier may include or beotherwise associated with an identifier corresponding to an entity typethat may be assigned to one or more sources of event messages. Forexample, the publisher identifier may include or otherwise be associatedwith an entity type such as “TRUSTED-VENDOR,” “ADMIN”, or the like.

After receiving a publisher identifier, the system may determine whetherthe publisher identifier is associated with one of a set of authorizedpublishers with respect to the event indicated by the event message. Insome embodiments, the system may refer to a set of authorized publisherscorresponding to the event indicated by the event message. For example,the event message may indicate that an event associated with the eventmessage “PAY DELIVERED” has occurred. In in response, the system maydetermine that the event satisfies an condition threshold, wheresatisfying the condition threshold may include a determination that theevent satisfies one or more norm conditions in an associative array ofconditions and that the associated publisher is authorized to deliverthe message. The associative array of conditions may include a list ofnorm conditions that, if satisfied, may result in triggering at leastone triggerable vertex of the smart contract. For example, the systemmay determine that the event “PAY DELIVERED” is a direct match with thenorm condition “if(PAY DELIVERED)” of the associative array ofconditions. In some embodiments, the system may then refer to the set ofauthorized publishers associated with the event “PAY DELIVERED.” Thesystem may then determine whether the publisher identifier is in the setof authorized publishers or otherwise associated with the set ofauthorized publishers, such as by having an entity type representing theset of authorized publishers. In some embodiments, if the systemdetermines that the event message is not authorized, the event messagemay be rejected as not authorized.

In some embodiments, the operation to authorize the event may include aoperations represented by Statement 1 or Statement 2 below, where “prop”may be a string value including an event and “pub” may be a string valuerepresenting a publisher identifier or entity type. In some embodiments,Statement 1 below may represent an authorization operation that includesthe arrival of an event E[pub] from publisher pub. The system may thencompare the publisher “P[E[pub]]” of the event “E[pub]” with each of aset of authorized publishers “D[E[prop]][pub]”, where each of the set ofauthorized publishers is authorized to publish the event “E[prop]”. Insome embodiments, the set of entities may include or otherwise beassociated with the set of authorized publishers. Statement 2 mayrepresent the situation which a plurality of entities may publish avalid event and the systems authorizes a message based on the entitytype “P[E[pub]][role]” being in the set of authorized publishers“D[E[prop]][pub],” where the set of authorized publishers“D[E[prop]][pub]” may include authorized publisher type:

D[E[prop]][pub]==P[E[pub]]  (1)

D[E[prop]][pub]=P[E[pub]][role]  (2)

In some embodiments, the set of authorized publishers may include a setof publisher identifiers, and the publisher identifier may in the set ofpublisher identifiers. For example, if the publisher identifier is“BLMBRG” and the set of authorized publishers include “BLMBRG,” thesystem may determine that an event message including the publisheridentifier “BLMBRG” is authorized. Alternatively, or in addition, theset of authorized publishers may include one or more authorized entitytypes and a respective publisher may be an authorized publisher if therespective publisher identifier is associated with the authorized entitytype. For example, if the publisher identifier is “BLMBRG,” and if theset of authorized publishers include the entity type “AUTH_PROVIDERS,”and if “BLMBRG” is associated with “AUTH_PROVIDERS” via an associativearray, then the system may determine that the publisher identifier isassociated with the set of authorized publishers. In response, thesystem may determine that the event message including the publisheridentifier “BLMBRG” is authorized. In some embodiments, the system maydetermine that one or more events indicated by the event message hasoccurred only after determining that the event message is authorized.

In some embodiments, the event message may include a signature valueusable by the system to compute a cryptographic hash value. Furthermore,some event messages may include the event payload with the signaturevalue (e.g., via string concatenation) to compute the cryptographic hashvalue. The system may use various cryptographic hashing algorithms suchas SHA-2, Bcrypt, Scrypt, or the like may be used to generate acryptographic hash value. In some embodiments, the system may usesalting operations or peppering operations to increase protection forpublisher information. In some embodiments, the system may retrieve acryptographic certificate based on a publisher identifier as describedabove and authenticate the event message after determining that on thecryptographic hash value satisfies one or more criteria based on thecryptographic certificate. A cryptographic certificate may include acryptographic public key used to compare with the cryptographic hashvalue, as further discussed below. In addition, the cryptographiccertificate may also include one or more second cryptographic valuesindicating a certificate issuer, certificate authority private key,other certificate metadata, or the like.

In some embodiments, a smart contract may include or be associated witha plurality of cryptographic certificates. The system may determine ofwhich cryptographic certificate to use may be based on a map of entitiesof the smart contract. In some embodiments, the operation toauthenticate the event may include a statement represented by Statement3 below, where “v” may represent a signature verification algorithm,E[sig] may represent a signature value of an event object “E,”“P[E[pub]]” may represent a data structure that includes the entity thathad published the event E, and P[E[pub]][cert] may represent acryptographic certificate value such as cryptographic public key:

v(E[sig],P[E[pub]][cert])==True  (3)

Various signature verification algorithms may be used to authenticate anevent message based on a signature value of the event message. Forexample, the system may determine that the cryptographic hash value isequal to the cryptographic certificate, and, in response, authenticatethe event message. In some embodiments, the system may determine thatone or more events indicated by the event message has occurred onlyafter authenticating the event message.

In some embodiments, the system may determine that an event has occurredbased on a determination that a condition expiration threshold has beenreached. One or more norms represented by norm vertices in the smartcontract may include a condition expiration threshold such as anobligation that must be fulfilled by a first date or a right thatexpires after a second date. For example, a smart contract instanceexecuting on the system may include a set of condition expirationthresholds, where the set of condition expiration thresholds may includespecific dates, specific datetimes, durations from a starting point,other measurements of time, other measurements of time intervals, or thelike. The system may check the set of condition expiration thresholds todetermine if any of the condition expiration thresholds have beensatisfied.

An event message may be transmitted under one of various types ofmessaging architecture. In some embodiments, the architecture may bebased on a representational state transfer (REST) system, where theevent message may be a request or response. For example, a system mayreceive a request that includes the event message, where the requestincludes a method identifier indicating that the event message is storedin the request. As an example, the system may receive a request thatincludes a “POST” method indicator, which indicates that data is in therequest message. In addition, the request my include a host identifier,where the host identifier indicates a host of the smart contract beingexecuted by the system. For example, the host identifier may indicate aspecific computing device, a web address, an IP address, a virtualserver executing on a distribute computing platform, a specific node ofa decentralized computing system, or the like.

In some embodiments, the architecture may be based on apublisher-subscriber architecture such as the architecture of theadvanced message queuing protocol (AMQP), where the event message may bea either a publisher message or subscriber message. For example, usingthe AMQP, a client publisher application may send an event message overa TCP layer to an AMQP server. The event message may include a routingkey, and the AMQP server may act as a protocol broker that distributesthe event message to the system based on the routing key after storingthe event message in a queue. In some embodiments, the system may be asubscriber to the client publisher application that sent the eventmessage.

In some embodiments, the process 100 includes determining which smartcontracts of a set of active smart contracts that will change statebased on the event, as indicated by block 108. As discussed above, insome embodiments, the system may determine that the event satisfies oneor more norm conditions, and, in response, determine that the instanceof the smart contract will change state. For example, as furtherdiscussed below, the system may determine that the event indicated“PAYLOAD 0105 PROVIDED” satisfies the norm condition represented by thecondition “IF DELIVERED(PAYLOAD),” In response, the system may determinethat the smart contract will change state. Alternatively, or inaddition, as discussed above, the system may determine that the eventdoes not satisfy one or more norm conditions but does satisfy acondition expiration threshold. In response, the system may determinethat the instance of the smart contract will change state based on theevent not satisfying one or more norm conditions while having satisfiedthe condition expiration threshold. Furthermore, while this disclosuremay recite the specific use of a smart contract program in certainsections, some embodiments may use, modify, or generate other symbolicAI programs in place of a smart contract, where symbolic A programs arefurther discussed below.

In some embodiments, the system may include or otherwise have access toa plurality of smart contracts or smart contract instances. The systemmay perform a lookup operation to select which of the smart contracts toaccess in response to determining that an event has occurred. In someoperations, the smart contract may compare an event to the associativearray of conditions corresponding to each of a set of smart contracts toselect of the set of smart contracts should be updated and filter outsmart contracts that would not change state based on the event. Thesystem may then update each of the smart contract instances associatedwith a changed norm status, as discussed further below. Furthermore, thesystem may then update the respective associative array of conditionscorresponding to the set of smart contracts. In some embodiments, anassociative array of conditions may include only a subset of normconditions associated with a smart contract, where each the subset ofnorm conditions is associated with a triggerable vertex of the smartcontract. In some embodiments, the system may first deduplicate the normconditions before performing a lookup operation to increase performanceefficiency. For example, after determining that an event has occurred,some embodiments may search through a deduplicated array of normconditions. For each norm condition that the event would trigger, thesystem may then update the one or more smart contracts associated withthe norm condition in the deduplicated array of norm conditions. Byselecting smart contracts from a plurality of smart contracts based onan array of norm conditions instead of applying the event to the normconditions associated with the norm vertices of each of the set of smartcontracts, the system may reduce computations required to update a setof smart contracts.

The smart contract or associated smart contract state data may be storedon various types of computing systems. In some embodiments, the smartcontract state data may be stored in a centralized computing system andthe associated smart contract may be executed by the centralizedcomputing system. Alternatively, or in addition, the smart contract orassociated smart contract state data may be stored on a distributedcomputing system (like a decentralized computing system) and theassociated smart contract may be executed using a decentralizedapplication. Fore example, the smart contract may be stored on andexecuted by a Turing-complete decentralized computing system operatingon a set of peer nodes, as further described below.

In some embodiments, the smart contract data may include or be otherwiseassociated with a set of entities, such as a set of entities encoded asan associative array of entities. The associative array of entities thatmay include one or more entities that may interact with or view at leasta portion of the data associated with the smart contract. In someembodiments, the associative array of entities may include a firstassociative array, where keys of the first associative array mayindicate specific smart contract entities (e.g. data observers,publishers, or the like), and where each of the keys may correspond witha submap containing entity data such as a full legal name, a legalidentifier such as a ISIN/CUSIP and an entity type of the entity such as“LENDER,” “BORROWER”, “AGENT,” “REGULATOR,” or the like. In someembodiments, one or more entities of the associative array of entitiesmay include or be associated with a cryptographic certificate such as acryptographic public key. As described above, the cryptographiccertificate may be used to authenticate an event message or othermessage. By including authorization or authentication operations, thesystem may reduce the risk that an unauthorized publisher sends an eventmessage or that the event message from a publisher is tampered withoutthe system determining that tampering had occurred. In addition,authorization or authentication operations increase the non-repudiationof event messages, reducing the risk that a publisher may later disclaimresponsibility for transmitting an event message.

In some embodiments, the smart contract may also include or otherwise beassociated a set of conditions, such as a set of conditions encoded asan associative array of conditions. In some embodiments, the associativearray of conditions may include a set of norm conditions and associatednorm information. In some embodiments, the set of norm conditions may berepresented by an associative array, where a respective key of theassociative array may be a respective norm condition or norm conditionidentifier. The corresponding values of the associative array mayinclude a natural language description of the corresponding conditionand one or more publisher identifiers allowed to indicate that an eventsatisfying the respective norm condition has occurred. In someembodiments, the publisher identifier may indicate a specific entity keyor an entity type. Furthermore, the smart contract may also include orotherwise be associated with a set of norm vertices or a set of graphedges connecting the vertices, as further described below.

In some embodiments, the process 100 includes deserializing a serializedarray of norm vertices to generate a deserialized directed graph, asindicated by block 112. In some embodiments, the smart contract mayinclude or otherwise be associated with a set of norm vertices encodedas a serialized graph in various data serialization formats, where thesmart contract may encode a part or all the norm vertices by encodingthe graph edges connecting the norm vertices The serialized graph mayinclude a representation of an array of subarrays. A data serializationformat may include non-hierarchical formats or flat-file formats, andmay be stored in a persistent storage. In some embodiments, a serializedarray of norm vertices may include numeral values, strings, strings ofbytes, or the like. For example, the array of norm vertices (or otherdata structures in program state) may be stored in a data serializationformat such as JSON, XML, YAML, XDR, property list format, HDF, netCDF,or the like. For example, an array may be decomposed into lists ordictionaries in JSON amenable to serialization. Each subarray of anarray of subarrays may include a pair of norm vertices representing adirected graph edge. For example, a subarray may include a first valueand a second value, where the first value may represent a tail vertex ofa directed graph edge, and where the second value may represent a headvertex of the directed graph edge. For example, a subarray may includethe value “[1,5]” where the first value “1” represents a tail vertexindicated by the index value “1” and “5” represents a head vertexindicated by the index value “5.” While in serialized form, the array ofnorm vertices may reduce memory requirements during data storageoperations and bandwidth requirements during data transfer operations.

In some embodiments, the serialized array of norm vertices may be usedto construct an adjacency matrix or an index-free adjacency list torepresent a deserialized directed graph during a deserializationoperation. In some embodiments, an adjacency matrix or adjacency listmay increase efficient graph rendering or computation operations. Insome embodiments, the deserialized directed graph may be stored in afaster layer of memory relative to the serialized graph, such as in anon-persistent memory layer. For example, the system may deserialize aserialized array of vertices stored in flash memory to a deserializeddirected graph stored in Level 3 cache. In some embodiments, as furtherdescribed below, instead of forming a directed graph that includes allof the norm vertices included in the serialized array of norm vertices,the system may instead form a directed graph from a subset of theserialized array of norm vertices. As described above, each norm vertexmay have an associated norm status indicating whether the norm vertex istriggerable. In response, the system may form a directed graph of thetriggerable vertices without rendering or otherwise processing one ormore norm vertices not indicated to be triggerable. Using this method, avertex that is included in the serialized array of vertices may beabsent in the directed graph stored in non-persistent memory. Byreducing the number of number of vertices in a deserialized directedgraph, the efficiency of querying and updating operations of the smartcontract may be increased.

In some embodiments, the system may include an initial set of normvertices that is distinct from the array of norm vertices. For example,some embodiments may determine that the smart contract had made a firstdetermination that an event had occurred. In some embodiments, thesystem may search the data associated with the smart contract to find aninitial set of norm vertices representing an initial state of the smartcontract. The system may then deserialize the initial set of normvertices when executing the smart contract and perform the operationsfurther described below. The system may then deserialize a differentarray of norm vertices during subsequent deserialization operations.

In some embodiments, the process 100 includes determining a set oftriggerable vertices based on the directed graph, as indicated by block120. In some embodiments, the system may determine the set oftriggerable vertices based on the directed graph stored innon-persistent memory by searching through the vertices of the directedgraph for each of the head vertices of the directed graph and assigningthese vertices as a set of head vertices. The system may then searchthrough the set of head vertices and filter out all head vertices thatare also tail vertices of the directed graph, where the remainingvertices may be the set of leaf vertices of the directed graph, whereeach of the leaf vertices represent a triggerable vertex. Thus, the setof leaf vertices determined may be used as the set of triggerablevertices.

Alternatively, in some embodiments, a vertex of the set of norm verticesmay include or otherwise be associated with a norm status indicatingwhether the vertex is triggerable or not. In some embodiments, thesystem may search through the directed graph for vertices that have anassociated norm status indicating that the respective vertex istriggerable. Alternatively, or in addition, the system may searchthrough a list of norm statuses associated with the vertices of theserialized array of norm vertices to determine which of the vertices istriggerable and determine the set of triggerable vertices. For example,in some embodiments, each norm vertex of a smart contract may have anassociated norm status indicating whether the vertex is triggerable ornot triggerable, where the vertices and their associated statuses may becollected into a map of vertex trigger states. The system may thenperform operations to traverse the map of vertex trigger states anddetermine the set of triggerable vertices by collecting the verticesassociated with a norm status indicating that the vertex is triggerable(e.g. with a boolean value, a numeric value, a string, or the like). Forexample, the system may perform operations represented by Statement 4below, where G may represent a graph and may be an array of subarrays g,where each subarray g may represent a norm vertex and may include a setof values that include the value assigned to the subarray element g[4],where the subarray element g[4] indicates a norm status, and “Active”indicates that the norm vertex associated with subarray g istriggerable, and A is the set of triggerable vertices:

A←{g∈G|g[4]=“Active”}  (4)

In some embodiments, the process 100 includes determining a set oftriggered vertices based on the set of triggerable vertices, asindicated by block 124. In some embodiments, the system may comparedetermine the set of triggered vertices based on which the normconditions associated with the vertices of the directed graph aresatisfied by the event. In some embodiments, a norm condition maydirectly include satisfying event. For example, a norm condition mayinclude “IF DELIVERED(PAYMENT),” where the function “DELIVERED” returnsa boolean value indicating whether a payment represented by the variable“PAYMENT” is true or false. The system may then determine that the normcondition is satisfied if “DELIVERED(PAYMENT)” returns the boolean value“True.” The system may then add the vertex associated with the normcondition to the set of triggered vertices. For example, the system mayperform operations represented by Statement 5 below, where “A” is theset of triggerable vertices determined above, and where each subarray“a” may represent a triggerable vertex and may include a set of valuesthat include the value assigned to the subarray element a[1], where thesubarray element a[1] indicates a condition, and “U” is the set oftriggered vertices, and “N” is an associative array that describes thepossible graph nodes that may be triggered, such that, for an eventprop, N[prop] may return a structure that contains defining details ofthe vertices associated with the event prop:

U←{α∈A|N[a[1]][prop]=E[prop]}  (5)

In some embodiments, the determination that an event satisfies a normcondition may be based on a categorization of a norm into logicalcategories. As further described below in FIG. 5, logical categories mayinclude values such as a “right,” “obligation,” “prohibition,”“permission,” or the like. In some embodiments, after a determinationthat an event triggers a norm condition, the generation of consequentnorms or norm status changes associated with a triggered vertex may bebased on the logical category.

In some embodiments, a snapshot contract status may be associated withthe smart contract and may be used to indicate a general state of thesmart contract. The snapshot contract status may indicate whether theobligations of a contract are being fulfilled or if any prohibitions ofthe contract are being violated. For example, in some embodiments,satisfying an obligation norm condition may result in an increase in thesnapshot contract status and triggering a prohibitions norm may resultin a negative change to the snapshot contract status.

In some embodiments, the process 100 includes performing one or moreoperations indicated by blocks 152, 154, 156, and 160 for each of therespective triggered vertex of the set of triggered vertices, asindicated by block 150. In some embodiments, the process 100 includesupdating the respective triggered vertex based on an event by updating anorm status associated with the respective triggered vertex, asindicated by block 152. Updating a respective triggered vertex mayinclude updating one or more norm statuses or other status valuesassociated with the respective triggered vertex. For example, a normstatus of the respective triggered vertex may be updated to include oneof the strings “SATISFIED,” “EXERCISED,” “FAILED,” or “CANCELED,” basedon the norm conditions associated with the respective triggered vertexhaving been satisfied, exercised, failed, or canceled, respectively. Insome embodiments, the system may update a norm status to indicate thatthe respective triggered vertex is not triggerable. For example, anobligation norm of a smart contract may be required to be satisfied onlyonce. In response, after determining that the norm condition associatedwith the obligation has been satisfied by an event, the system mayupdate a first status value associated with the respective triggeredvertex to “false,” where the first status value indicates whether therespective triggered vertex is triggerable. In some embodiments, the oneor more status values may include a valence value indicating the numberof connections from the respective triggered vertex to another vertices,the number of connections to the respective triggered vertex from othervertices, or the like. As further described below, in some embodiments,the valence value or other status value associated with the respectivetriggered vertex may be updated after performing operations associatedwith the adjacent vertices of the respective triggered vertex.

In some embodiments, the process 100 includes determining whether arespective adjacent vertex of the respective triggered vertex should beset to be triggerable, as indicated by block 154. In some embodiments,the respective triggered vertex may include a pointer to or otherwise beassociated with a set of adjacent vertices, where each of the set ofadjacent vertices represent a norm of the smart contract that are set tooccur after the respective triggered vertex is triggered. In someembodiments, the system may determine whether an adjacent vertex of arespective triggered vertex should be set as triggerable based onspecific conditions associated with the adjacent vertex. For example, arespective triggered vertex may include program code instructing that afirst set of adjacent vertices should be set to be triggerable if afirst set of conditions are satisfied and that a second set of adjacentvertices should be set to be triggerable if a second set of conditionsare satisfied, where the first set of adjacent vertices are distinctfrom the second set of adjacent vertices. Alternatively, or in addition,the respective triggered vertex may include program instructing that athird set of adjacent vertices should be set to be triggerable if thefirst set of conditions are not satisfied but an associated conditionexpiration threshold is satisfied.

In some embodiments, the process 100 includes updating the respectiveadjacent vertex based on the event, as indicated by block 156. Updatingthe respective adjacent vertex based on the event may include settingone or more norm statuses associated with the adjacent vertex toindicate that the respective adjacent vertex is triggerable. Forexample, after a determination that a respective adjacent vertexassociated with a permission norm is to be set to be triggerable, a normstatus associated with the respective adjacent vertex may be updated tothe value “triggerable.”

In some embodiments, the process 100 includes determining whether anyadditional triggered vertices are available, as indicated by block 160.In some embodiments, the system may determine that additional triggeredvertices are available based on a determination that an iterative loopused to cycle through each the triggered vertices has not reached atermination condition. In response to a determination that additionaltriggered vertices are available, the process 100 may return to theoperations of block 150. Otherwise, operations of the process 100 mayproceed to block 164.

In some embodiments, the process 100 includes updating the directedgraph based on the updated triggered vertices or the respective adjacentvertices, as indicated by block 170. In some embodiments, updating thedirected graph may include updating an adjacency matrix or adjacencylist representing the directed based on each of the triggered verticesor their respective adjacent vertices. In some embodiments, instead oflooping through each updated vertex and then updating the directedgraph, the system may update the directed graph during or after eachupdate cycle. For example, after updating the respective triggeredvertex as described in block 156, the system may update the deserializeddirected graph.

In some embodiments, the process 100 includes updating the serializedarray of norm vertices or other smart contract state data based on thedirected graph and updated vertices, as indicated by block 174. In someembodiments, updating the serialized array of norm vertices may includeserializing the directed graph into a data serialization format, asdescribed above. In some embodiments, the data serialization format maybe the same as the data serialization format used when performingoperations described for block 112. For example, the system mayimplement a depth-first search (DFS) over the deserialized directedgraph to record distinct edge pairs and update the serialized array ofnorm vertices by either modifying or replacing the serialized array ofnorm vertices.

In some embodiments, the system may update a knowledge set based on theevent and smart contract state changes that occurred in response to theevent. In some embodiments, the knowledge set may include a set ofprevious events. The set of previous events may be encoded as a list ofprevious events. The list of previous events may include a subarray,where each subarray includes an event identifier of a recorded event orinformation associated with the recorded event. For example, the list ofprevious events may include a date and time during which an eventoccurred, an event identifier, one or more norm conditions satisfied bythe event, or the like. In some embodiments, a norm condition may bebased on the list of previous events. For example, a norm condition mayinclude a determination of whether an event type had occurred twicewithin a time duration based on the list of previous events. In someembodiments, the knowledge set may include a set of previously-triggeredvertices, where the set of previously-triggered vertices may be encodedas an array of previously-triggered vertices. In some embodiments, thesystem may further update the knowledge set by updating the array ofpreviously-triggered vertices based on the triggered vertices describedabove. For example, after updating a respective triggered vertex asdescribed above, the system may update the array of previously-triggeredvertices to include the respective triggered vertex. The array ofpreviously-triggered vertices may include a vertex identifier associatedwith the respective triggered vertex, an event identifier associatedwith the event that triggered the respective triggered vertex, and a setof values identifying the vertices that are set to be triggerable aftertriggering the respective triggered vertex.

In some embodiments, the process 100 includes persisting the updatedserialized array of norm vertices or other smart contract data tostorage, as indicated by block 178. In some embodiments, persisting thesmart contract data to storage may include updating the memory storagein a single computing device or a computing device of a centralizedcomputing system. Alternatively, or in addition, persisting the smartcontract data to storage may include storing the smart contract data toa decentralized tamper-evident data store. In some embodiments, bystoring the serialized array of norm vertices in a decentralizedtamper-evident data store instead of storing a deserialized directedgraph in the decentralized tamper-evident data store, the system mayincrease the efficiency and performance of the data distribution amongstthe nodes of the decentralized tamper-evident data store. Furthermore,in some embodiments, triggering a norm vertex may include triggering asmart contract termination action. When a smart contract terminationaction is triggered, vertices other than the respective triggered vertexmay be updated to set the statuses of each vertex of these othervertices as not triggerable, even if these other vertices are notdirectly connected to the triggered vertex.

In some embodiments, the system may display a visualization of the smartcontract state. For example, the system may display a visualization ofsmart contract state as a directed graph, such as (though not limitedto) those shown in FIG. 5-10, 17-18, or 20 below, where the vertices mayhave different colors based on norm status and/or logical category.Alternatively, or in addition, the system may generate other types ofvisualizations of the smart contract state. For example, the system maydisplay a pie chart representing of a plurality of smart contract typesthat indicate which type of the smart contracts have the highest amountof associated cost.

In some embodiments, the process 100 or other processes described inthis disclosure may execute on a decentralized computing platformcapable of persisting state to a decentralized tamper-evident datastore. Furthermore, in some embodiments, the decentralized computingplatform may be capable of executing various programs, such as smartcontracts, on the computing platform in a decentralized, verifiablemanner. For example, each of a set of peer nodes of the computingplatform may perform the same computations, and a consensus may bereached regarding results of the computation. In some embodiments,various consensus algorithms (e.g., Raft, Paxos, Helix, Hotstuff,Practical Byzantine Fault Tolerance, Honey Badger Byzantine FaultTolerance, or the like) may be implemented to determine states orcomputation results of the various programs executed on thedecentralized computing platform without requiring that any onecomputing device be a trusted device (e.g., require an assumption thatthe computing device's computation results are correct). The one or moreconsensus algorithms used may be selected or altered to impede an entityfrom modifying, corrupting, or otherwise altering results of thecomputation by peer nodes not under the entity's control. Examples of adecentralized tamper-evident data store may include Interplanetary FileSystem, Blockstack, Swarm, or the like. Examples of a decentralizedcomputing platform may include Hyperledger (e.g., Sawtooth, Fabric, orIroha, or the like), Stellar, Ethereum, EOS, Bitcoin, Corda, Libra, NEO,or Openchain.

FIG. 2 depicts a data model of program state data, in accordance withsome embodiments of the present techniques. In some embodiments, a smartcontract may include or otherwise be associated with program state datasuch as smart contract state data 200. The smart contract state data 200includes an associative array of entities 210, an associative array ofconditions 220, an associative array of norms 230, a graph list 240, anda knowledge list 250. The associative array of entities 210 may includea set of keys, each key representing an entity capable of interactingwith or observing smart contract data. For example, a publisherproviding an event message to the smart contract may be an entity. Thecorresponding value of a key of the associative array of entities 210may include a submap that includes values for a name, a legal identifiervalue (e.g., a ISIN/CUSIP identifier), an entity type for authorizationoperations, and a public key for authentication operations (e.g., acryptographic public key). In some embodiments, the name, identifiervalue, entity type, or public keys may be used in the authorization andauthentication operations discussed for block 104.

The associative array of conditions 220 may include a set of keys, whereeach key represents an event that may trigger at least one triggerablevertex that would result in a change in norm status, and where acorresponding value of each key includes an events submap. The eventssubmap may include a publisher identifier. As shown by the link 221, thepublisher identifier may be used as a reference to the key of theassociative array of entities. Alternatively, or in addition, the eventssubmap may include a subject identifier, which may include natural textlanguage to provide a context for the corresponding event.

The associative array of norms 230 may include a set of keys, where eachkey may represent a norm of the smart contract, which may be associatedwith as a norm vertex in a graph, norm conditions and consequent norms.In some embodiments, the consequent norms may themselves be associatedwith their own norm vertices. Each value corresponding to the norm mayinclude a norms submap that includes one or more norm conditions thatmay be used to trigger the norm by satisfying a norm condition, or bynot satisfying the norm condition after satisfying condition expirationthreshold associated with the norm. As shown by the link 231, the normconditions may include a norm identifier that may be used as a referenceto a key of the associative array of conditions 220. The norms submapmay also include an entity identifier, where the entity identifier maybe used as reference to a key of the associative array of entities 210,as shown by the link 232. The norm may also include a conditionexpiration threshold, which may be represented by the “expiry” fieldshown in the associative array of norms 230. As discussed above, someembodiments may result in a norm status change or trigger other updatesto a vertex if a norm condition is not satisfied but the conditionexpiration threshold is satisfied. The norm submap may also include aconsequences list, where the consequences list may include set ofsublists that includes a tail vertex representing a consequent norm thatbecome triggerable, a head vertex of the new norm (which may be thetriggered norm), and a label.

In some embodiments, a smart contract state may initially construct thegraph list 240 in a first iteration based on the associative array ofnorms 230 and update the graph list 240 based on a previous iteration ofthe graph list 240. As described above, the graph list may be in aserialized form, such as a serialized array of norm vertices written inthe YAML markup language. As discussed above, the graph list 240 may bea list of graph sublists, where each sublist includes a tail vertexvalue, a head vertex value, a label associated with the graph edgeconnecting the tail vertex with the head vertex, a group identifier, anda norm status value. In some embodiments, the norm status may includevalues such as “satisfied,” “exercised,” failed, “active,” “terminal,”“canceled,” “triggerable,” or “untriggerable.” In some embodiments, anorm vertex may be associated with more than one norm status. As shownby link 241, a tail vertex of the graph may be linked to a norm in theassociative array of norms 230. Similarly, as shown by the links242-243, the tail and head vertices of the graph list 240 may beassociated with a listed tail norm or head norm in the associative arrayof norms 230 for a respective norm. Furthermore, as shown by the link244, the group identifier listed in a graph sublist may also beassociated with a value in the associative array of norms 230, such aswith a key in the associative array of norms 230.

In some embodiments, a smart contract state may initially construct theknowledge list 250 in a first iteration based on the associative arrayof norms 230 and update the knowledge list 250 based on smart contractstate changes. The knowledge list 250 may be sequentially ordered intime (e.g. a time when a norm status changes, a time when an event isreceived, or the like). In some embodiments, each entry of the knowledgelist 250 may include an identifier “eid,” an event time “etime,” apublisher identifier associated with an event that triggered a normvertex, the event that triggered the norm vertex. In addition, theknowledge list 250 may include various other data related to the smartcontract state change, such as a field “negation” to indicate whether anevent is negated, a field “ptime” in ISO8601 format to represent ansub-event time (e.g. for event that require multiple sub-events totrigger a norm vertex), a field “signature” to provide a signature valuethat allows authentication against the public key held by a publisherfor later data authentication operations or data forensics operations.In some embodiments, the knowledge list 250 may include an evidencelist, where the evidence list may include a base64 encoded blob, anevidence type containing a string describing the file type of thedecoded evidence, and a field for descriptive purposes. In someembodiments, the evidence list may be used for additional safety orverification during transactions.

Outcome Measurement and Prediction

In some embodiments, outcomes of symbolic AI models (like thetechnology-based self-executing protocols discussed in this disclosure,expert systems, and others) may be simulated and characterized invarious ways that are useful for understanding complex systems. Examplesof symbolic AI systems include systems that may determine a set ofoutputs from a set of inputs using one or more lookup tables, graphs(e.g. a decision tree), logical systems, or other interpretable Asystems (which may include non-interpretable sub-components or bepipelined with non-interpretable models). The data models, norms, orother elements described in this disclosure constitute an example of asymbolic AI model. Some embodiments may use a symbolic AI model (like aset of smart contracts) in order to predict possible outcomes of themodel and determine associated probability distributions for the set ofpossible outcomes (or various population statistics). Features of asymbolic AI model that incorporates elements of data model described inthis disclosure may increase the efficiency of smart contract searches.In addition, the use of logical categories (e.g., “right,” “permission,”“obligation”) describing the relationships between conditionalstatements (or other logical units) of a smart contract may allow theaccurate prediction of (or sampling of) outcomes across a population ofdifferently-structured smart contracts without requiring atime-consuming analysis of each of the contexts of individual smartcontracts from the population of differently-structured smart contracts.Furthermore, the operations of a symbolic AI model may be used topredict outcomes (e.g., of a smart contract, or call graph of such smartcontracts) and may be tracked to logical units (like conditionalstatements, such as rules of a smart contract). These predicted outcomesmay be explainable to an external observer in the context of the termsof the logical units of symbolic AI models, which may be useful inmedical fields, legal fields, robotics, dev ops, financial fields, orother fields of industry or research.

In some embodiments, the symbolic AI model may include the use of scoresfor a single smart contract or a plurality of smart contracts, where thescore may represent various values, like a range of movement along adegree of freedom of an industrial robot, an amount of computer memoryto be allocated, an amount of processing time that a first entity owes asecond entity, an amount to be changed between two entities, a totalamount stored by an entity, or the like. A symbolic AI model may includescores of different type. Changes in scores of different type may occurconcurrently when modeling an interaction between different entities.For example, a first score type may represent an amount of computermemory to be stored within a first duration and a second score type mayrepresent an amount of computer memory to stored within a secondduration that occurs after the first duration. A smart contract may beused to allocate computer memory across two different entities tooptimize memory use across the entity domains. Possible outcomes andwith respect to memory allocation across the two domains may besimulated. Alternatively, or in addition, exchanges in other computingresources of the same type or different types may be simulated withscores in a symbolic AI model. For example, a symbolic A model mayinclude a first score and as second score, where the first score mayrepresent an amount of bandwidth available for communication between afirst entity or second entity and a third entity, and where the secondscore may represent an amount of memory available for use by the firstor second entity. The outcome of an exchange negotiated via a smartcontract between the first and second entity for bandwidth and memoryallocation may then be simulated to predict wireless computing resourcedistribution during operations of a distributed data structure across awireless network.

In some embodiments, simulating outcomes of may include processing oneor more norm vertices representing one or more norms of a smart contractas described in this disclosure. For example, the symbolic AI model mayinclude an object representing a norm vertex, where the object includesa first score representing an amount owed to a first entity and a secondscore representing an amount that would be automatically transferred tothe first entity (e.g., as a down payment). In some embodiments, thesymbolic AI model may incorporate the entirety of a smart contract andits associated data model when performing simulations based on the smartcontract. For example, a symbolic AI model may include one or moredirected graphs of to represent the state of a data model.Alternatively, or in addition, some embodiments may include more datathan the smart contract being simulated or less data than the smartcontract be simulated.

In some embodiments, the symbolic AI system (a term used interchangeablywith symbolic AI model) may process the conditional statements (or otherlogical units) associated with each of the norms of a smart contract toincrease simulation efficiency by extracting only quantitative changesand making simplifying assumptions about score changes. For example, asystem may collect the norm conditions and associated outcomesubroutines associated with each of a set of norm vertices and extractonly the changes in an amount of currency owed as a first score andchanges in an amount of currency transferred as a seconds score whenincorporating this information into the conditions of the symbolic AImodel. In some embodiments, the information reduction may increasecomputation efficiency by removing information from the analysis of asmart contract determined to be not pertinent to a selected score. Someembodiments simulate outcomes across a plurality of smart contractsusing a standardized search and simulation heuristic, and the systemdescribed herein may provide a population of scores, where thepopulation of scores may be the plurality of outcome scores determinedfrom a simulation of each of the smart contracts or values computed fromthe plurality of outcome scores. For example, values determined based onthe population of scores may include parameters of a probabilitydistribution of the scores, a total score value, a measure of centraltendency (e.g. median score value, mean score value, etc.), or the like.

In some embodiments, the symbolic AI model may be an un-instantiatedsmart contract or may be a transformation thereof, e.g., approximatingthe smart contract. For example, as further described below, the systemmay instantiate a program instance that includes a symbolic AI modelbased on a selected smart contract that is not yet instantiated.Alternatively, a symbolic A model may be determined based on aninstantiated smart contract. For example, the system may select aninstantiated smart contract with a program state that has alreadychanged from its initial program state in order to determine futurepossible outcomes in the context of the existing changes. The system maythen copy or otherwise use a simulated version of the changed programstate when simulating the instantiated smart contract. For example, thesystem may select an instantiated smart contract for simulation with asymbolic AI system and deserialize a directed graph of the instantiatedsmart contract. The symbolic AI system may copy the deserializeddirected graph to generate a simulation of the directed graph, where thenodes of the simulated directed graph are associated with simplifiedconditional statements that convert quantifiable changes into scores andare stripped of non-quantifiable changes in comparison to theconditional statements of the smart contract.

FIG. 3 is flowchart of an example of a process by which a program maysimulate outcomes or outcome scores of symbolic AI models, in accordancewith some embodiments of the present techniques. In some embodiments, aprocess 300 includes selecting a set of smart contracts (or othersymbolic AI models) based on a search parameter, as indicated by block304. In some embodiments, a system may include or otherwise have accessto a plurality of smart contracts or smart contract instances, and thesystem may select a set of smart contracts from the plurality based on aspecific search parameter, such as an entity, entity type, event, eventtype, or keyword. For example, the system may perform a lookup operationto select which of the smart contracts to access based an event. Duringthe lookup operation, the system may compare an event to the associativearrays of conditions corresponding to each of a plurality of smartcontracts and select a set of smart contracts based on which of thesmart contracts would change state in response to receiving the event.Some embodiments may crawl a call graph (of calls between smartcontracts, or other symbolic AI models) to select additional smartcontracts.

In addition, or alternatively, the system may perform a lookup operationto select which of the smart contracts to access based on an entity orentity type. For example, the system may compare an entity to theassociative arrays of entities corresponding to each of a plurality ofsmart contracts and select a set of smart contracts based on which ofthe corresponding arrays of entities include the entity. An entityidentifier may be in an array of entities or some other set of entitiesif an entity type associated with the entity identifier is in the arrayof entities. For example, if the entity “BLMBRG” has an associatedentity type of “trusted publisher,” some embodiments may determine that“BLMBRG” is in the set of entities of a smart contract if the entitytype “trusted publisher” is listed in the set of entities.Alternatively, some embodiments may require that the exact entityidentifier be listed in a set of entities before determining that theentity identifier in the set of entities. For example, some embodimentsmay determine that “BLMBRG” is in a set of entities of a smart contractonly if “BLMBRG” is one of the elements of the set of entities.Furthermore, in some embodiments, the search may include intermediaryentities between two different entities, where intermediary smartcontract may be a smart contract (other than the first or second smartcontract) that has relationships with both the first and secondentities. For example, a search for smart contracts relating a firstentity and a second entity may return a set smart contracts that includea first smart contract and a second smart contract, where the array ofentities of the first smart contract includes the first entity and anintermediary entity, and where the array of entities of the second smartcontract includes the second entity and the intermediary entity.

In some embodiments, an intermediary entity for a first entity and asecond entity may be found by determining the intersection of entitiesbetween a first set of smart contracts associated with the first entityand a second set of smart contracts associated with the second entity.For example, the system may select a first set of smart contracts from aplurality of smart contracts based on which sets of entities associatedwith plurality of smart contracts include the first entity. Similarly,the system may select a second set of smart contracts from a pluralityof smart contracts based on which sets of entities associated withplurality of smart contracts include the second entity. The system maythen determine the intersection of entities by searching through thesets of entities of the first and second set of smart contracts tocollect the entities that appear in both the first set and second setand determine that these collected entities are intermediary entities.In some embodiments, as further described below, additional methods arepossible to determine a set of smart contracts associating a firstentity with a second entity in order to quantify a relationship betweenthe first entity and the second entity.

As discussed in this disclosure, some embodiments may crawl a call graphto select additional smart contracts based on possible relationshipsbetween a first entity and a second entity. The call graph may be aprivity graph, which may track privity relations between the firstentity and entities other than the second entity in order to determineor quantify relations between the first entity and the second entity. ifFor example, some embodiments may crawl through a privity graph ofpossible score changes across multiple contracts and determine aquantitative score relationship between a first entity and a secondentity based on a first transaction between the first entity and a thirdentity, a second transaction between the third entity and a fourthentity, a third transaction between the fourth entity and a fifthentity, and a fourth transaction between the fifth entity and the secondentity.

In some embodiments, the process 300 includes performing one or moreoperations indicated by blocks 312, 316, 320, 324, 328, 336, 340, 344,and 350 for each of the respective smart contracts or other programs ofthe selected set of smart contracts or other programs, as indicated byblock 308. As further discussed below, the one or more outputs fromexecuting each of the smart contracts may be used to determine apopulation of scores of multiple smart contracts. As used herein, thepopulation of scores of multiple smart contracts may represent one ormore population metric values calculated from scores of the smartcontract. For example, the population of scores of multiple smartcontracts may include a measure of central tendency, a measure ofdispersion, a kurtosis value, a parameter of a statistical distribution,one or more values of histogram, or the like. Furthermore, in someembodiments, the process 300 may include performing one or moreoperations in parallel using multiple processor cores, where performingmultiple operations in parallel may include performing the multipleoperations concurrently. For example, some embodiments may perform theoperations of the blocks 312, 316, 320, 324, 328, 336, 340, 344, and 350for a plurality of smart contracts in parallel by using one or moreprocessors for each of the plurality of smart contracts. By performingoperations in parallel, computation times may be significantly reduced.

In some embodiments, the process 300 includes acquiring a set ofconditional statements (or other logical units), set of entities, set ofindices indexing the conditional statements, or other data associatedwith the selected smart contract, as indicated by block 312. Each of theset of conditional statements may be associated with an index value andmay include or be otherwise associated with a respective set ofconditions and a respective set of outcome subroutines, where acomputing device may execute the respective set of outcome subroutinesin response to an event satisfying the respective set of conditions. Insome embodiments, the set of conditional statements may form a network,like a tree structure, with respect to each other. For example, anoutcome subroutine of one the conditional statements may include areference to or otherwise use an index value associated with anotherconditional statement. In some embodiments, the set of conditionalstatements and set of indices may be acquired from a data model, wherethe index values may be or otherwise correspond to the identifiers fornorm vertices of a directed graph. For example, the set of conditionalstatements and set of indices may be acquired from the associative arrayof norms 230, the associative array of conditions 220, and the graphlist 240. Alternatively, the system may acquire the conditionalstatements and indices from data stored using other data models. Forexample, the system may acquire the conditional statements from anindexed array of objects, where each object may include a method thatcan take an event as a parameter, test the event based on a condition ofthe method, and return a set of values or include a reference to anotherobject of the array. The system may use the indices of the indexed arrayas the indices of the conditional statements and parse the methods toprovide the set of conditional statements.

In some embodiments, the process 300 includes instantiating or otherwiseexecuting a program instance having program state data that includes asymbolic AI model that includes values from the data associated with theselected smart contract, as indicated by block 316. In some embodiments,the symbolic AI model may include graph vertices associated with the setof conditional statements described in this disclosure and may alsoinclude directed graph edges connecting the graph vertices. In addition,or alternatively, the symbolic AI model may include a set of tables,decision trees, graphs, or logical systems to provide a predicted valueas an output based on one or more inputs corresponding to real orsimulated events. For example, the system may traverse the directedgraph of a symbolic AI model to determine which nodes of the directedgraph to visit based on a decision tree of the symbolic A model.Furthermore, in some embodiments, the symbolic AI system may bere-instantiated or be modified in real-time in response to a particularevent message updating a smart contract being simulated. For example, aninstantiated smart contract may be executing and concurrently beingsimulated by a symbolic A system. In response to the smart contractreceiving an event message, the symbolic A system may determine a newset of events based on the event message and update its own programstate such that its new initial state is based on the smart contractprogram state after the smart contract program state has been updated bythe events of the event message.

In some embodiments, the symbolic AI model may include a graph. In someembodiments, the system may generate a graph list such as the graph list240 using the methods discussed in this disclosure. In some embodiments,the program instance may be a local version of a selected smart contractand have program state data identical to program state data in theselected smart contract. Alternatively, the program instance may includeprogram data not included in the smart contract or exclude data includedin the smart contract. In some embodiments, the graph of the symbolic AImodel may include a set of graph vertices and a set of directed graphedges connecting the graph vertices, where each of the graph verticesmay be identified by an identifier and corresponds to a conditionalstatement of a smart contract. In some embodiments, the identifier maybe the set of index values associated with the conditional statements ofthe smart contract. Alternatively, the identifier may be different fromthe set of index values associated with the conditional statements ofthe smart contract. For example, the system may choose a set ofidentifiers that are different from the set of index values to increasesystem efficiency or reduce memory use.

In some embodiments, the directed graph edges may be structured toprovide directional information about the graph vertices of a symbolicAI model. For example, a directed graph edge may be represented as anarray of identifier pairs. The first element of each of the identifierpairs may be treated as a tail vertex by the symbolic A system and thesecond element of the identifier pairs may be treated as a head vertexby the symbolic A system. In some embodiments, the selected smartcontract may already be in the process of being executed and the programstate data of the program instance may include the norm statuses andscores of the smart contract state. For example, the program state datamay be copied directly from the state data of a selected smart contract,where the changes effected by the outcome subroutines may be treated asscores.

A smart contract score may represent one of various types of values. Forexample, a smart contract score may represent a reputation score of anentity in a social network, a cryptocurrency value such as an amount ofcryptocurrency, an amount of electrical energy, an amount of computingeffort such as Ethereum's Gas, an amount of computing memory, or thelike. A smart contract score may represent an objective value associatedwith an entity, such as an available amount of computing memoryassociated with the entity. Alternatively, a smart contract score mayrepresent an amount by which a stored value is to be changed, such as acredit amount transferred from a first entity to a second entity.

In some embodiments, a program state may keep track of a plurality ofscores. For example, a vertex of a directed graph of a symbolic A modelmay include or otherwise be associated with a first score representingan amount of possessed by a first entity, a second score representing anamount owed to or owed by the first entity, a third score representingan amount possessed by a second entity, and a fourth score representingan amount owed to or owed by the second entity. In some embodiments, aconditional statement may be parsed to determine outcome scores. Forexample, an outcome subroutine associated with a vertex of a graph ofthe symbolic AI model may include instructions that a first entity isobligated provide 30 cryptocurrency units to a second entity and thatthe second entity is obligated to send a message to the first entitywith an electronic receipt, and the system may determine that anassociated score of the first vertex is equal to 30 and also determinethat no score value is needed for the sending of the message. As furtherdiscussed below, by keeping track of scores and score changes, entirepopulations of smart contracts may be analyzed with greater accuracywithout requiring a deep understanding of the specific terms or entitybehaviors of any specific contract.

In some embodiments, a symbolic AI model may include statusescorresponding to each of a set of vertices representing the norms of asmart contract. The symbolic A model statuses may use the samecategories as the norm statuses of a smart contract. Furthermore, thesymbolic AI model status for a vertex may be identical to or beotherwise based on the status for the corresponding norm vertex beingsimulated. For example, if a norm status for a first norm vertex of asmart contract is “triggered-satisfied,” the symbolic AI model statusfor a first symbolic AI model vertex corresponding to the first normvertex may also be “triggered-satisfied.” Alternatively, the system mayselect a different categorical value for a symbolic AI model vertexstatus that is still based on the corresponding norm status. Similarly,the symbolic AI model may include vertex categories similar to oridentical to the logical categories associated with of the set of normvertices of a smart contract. Furthermore, the symbolic AI model vertexcategory may be identical to or be otherwise based on the logical forthe corresponding norm vertex being simulated. For example, if a logicalcategory for a first norm vertex of a smart contract is “Rights” thesymbolic AI model category for a first symbolic A model vertex (“vertexcategory”) corresponding to the first norm vertex may also be “Rights.”Alternatively, the system may select a different categorical value for avertex category that is still based on the corresponding logicalcategory.

In some embodiments, the instantiated program may be a smart contractthat may use or otherwise process events. Alternatively, or in addition,the program instance may be a modeling application and not an instanceof the selected smart contract itself. For example, a symbolic AI systemmay be a modeling application that determines the values of acorresponding symbolic AI model based on the conditional statements of asmart contract without requiring that an event message be sent to an APIof the modeling application. In some embodiments, the program instanceof the symbolic AI system may change program state without performingone or more operations used by the smart contract that the programinstance is based on. For example, the program instance of the symbolicAI system may change its program state data without deserializingserialized smart contract data, even if the smart contract that theprogram instance is based on includes operations to deserializeserialized smart contract data. In some embodiments, the program statedata may be stored using a data model similar to that described in thisdisclosure for FIG. 2. Alternatively, or in addition, the program statedata may be stored in various other ways. For example, instead ofstoring values in separate arrays, the program instance may store thenorm conditions, norm outcome actions, and their relationships to eachother as part of a same array.

In some embodiments, the process 300 includes performing one or moreiterations of the operations indicated by blocks 320, 324, 328, 332,336, and 340 for each of the respective smart contracts or otherprograms of the selected set of smart contracts or other programs, asindicated by block 320. Furthermore, in some embodiments, the process300 may include performing the one or more iterations in parallel usingmultiple processor cores. For example, some embodiments may includeperforming multiple iterations of the operations of the blocks 320, 324,328, 332, 336, or 340 for multiple iterations in parallel using aplurality of processor cores. By performing the multiple iterations ofthe operations in parallel, computation times may be significantlyreduced.

In some embodiments, the system may perform one or more iterations ofoperations to modify the statuses of a first set of vertices and thenupdate the program state data based on the modified statuses in order toacquire a plurality of outcomes. The program state data or a portion ofthe program state data may be in a same state at the start eachiteration, where two states of program state data are identical if bothstates have the same set of values. For example, if a first state ofprogram state data is [1,2,3], and if a second state of program statedata is [1,2,4], and if the program state data is reverted to [1,2,3],the reverted program state data may be described as being in the firststate. In some embodiments, the system may execute the smart contract orsmart contract simulation for a pre-determined number of iterations.Alternatively, or in addition, as further recited below, the smartcontract or smart contract simulation may be repeatedly executed until aset of iteration stopping criteria are achieved. As further discussedbelow, the plurality of outcomes corresponding to the plurality ofiterations may be used to provide one or more multi-iteration scoresusable for decision-support systems and for determining multi-protocolscores.

In some embodiments, the system may modify one or more statusesassociated with the vertices of the graph of the symbolic AI model basedon a scenario and update the program state data based on the modifiedstatuses, as indicated by block 328. In some embodiments, the scenariomay be a set of inputs based on events. For example, a scenario mayinclude simulated events or simulated event messages that may betestable by the conditions of a conditional statement. In response, afirst vertex of the program instance may compare the simulated event toa condition and determine that a second vertex of the symbolic AI modelof the should be activated. For example, an input may include an event“entity A transmitted data 0x104ABC to entity C,” which may satisfy acondition and change a status associated with a first vertex associatedwith the conditional statement to “satisfied.” As discussed below, thesystem may then update the symbolic AI model based on the status changeby activating an adjacent vertex to the first vertex.

Alternatively, or in addition, an input may include a message to changea program state without including an event that satisfies the normconditions associated with the norm. For example, the input may includedirect instructions interpretable by a symbolic AI system to set avertex status to indicate that the corresponding vertex is triggered anddirect which of a set of outcome subroutines to execute. The system maythen update the symbolic AI model by activating one or more adjacentvertices described by the subset of outcome subroutines to execute.

In some embodiments, the scenario may include a single input.Alternatively, the scenario may include a sequence of inputs. Forexample, the scenario may include a first event, second event, and thirdevent in sequential order. In some embodiments, the set of events may begenerated using a Monte Carlo simulator. Some embodiments may randomlydetermine subsequent states from an initial state based on one or moreprobability distributions associated with each state of a set ofpossible subsequent states with respect to a previous state, where theprobability distributions may be based on scores and logical categoriesassociated with the set of possible states. For example, the programstate may be in a state where only two subsequent possible states arepossible, where the first subsequent possible state includes triggeringa rights norm and a second subsequent possible state includes triggeringan obligations norm.

In some embodiments, one or more inputs of a scenario may be determinedusing a decision tree. In some embodiments, a decision tree may be usedto provide a set of decision based on scores, logical categories,statuses, and other factors associated with the active vertices of asimulated smart contract state. For example, a symbolic AI system maydetermine that the two possible states for a smart contract may resultfrom either exercising a first rights norm or exercising of a secondrights norm. A decision tree may be used to compare the logicalcategories, the scores associated with each norm, and the otherinformation related to the active norms to determine which rights norman entity would be most likely to exercise. In some embodiments, thesymbolic AI system may compare a first score associated with a possiblestate represented by a first tree node with a second score of adifferent possible state represented by a second tree node. In responseto the first score being greater than the second score, the symbolic Asystem may determine a simulated input that will result in the futurestate represented by the first tree node. Furthermore, in someembodiments, the decision tree may incorporate probability distributionsor other decision-influencing factors to more accurately simulatereal-world scenarios.

Alternatively, or in addition, some embodiments may include a MonteCarlo Tree Search (MCTS) method to generate a random sequence of eventsbased on a set of possible events and a probability distribution bywhich the events may occur. The operations of the simulation may be mademore efficient by selecting events that known to satisfy at least onecondition of the set of conditional statements of the smart contractbeing simulated. In some embodiments, a symbolic AI system may determinea set of events for a smart contract simulation by determining a firstsimulated input based on a set of weighting values assigned to verticesof a graph of a symbolic AI model associated with norms of the smartcontract. In some embodiments, the system may further determine asimulated input based on a count of the number of iterations of thesimulation performed so far.

The system may then update the symbolic AI model based on the firstsimulated input, advancing the symbolic AI model to a second state. Forexample, after changing the status of a first vertex associated with anobligations norm from “unrealized” to “failed,” the symbolic AI modelmay then activate a first adjacent vertex representing a rights norm anda second adjacent vertex representing an prohibitions norm, where bothadjacent vertices are adjacent to the first vertex. The symbolic AIsystem may then determine a second simulated input, wherein the secondsimulated input may be selected based on weighting value correspondingto each of the first adjacent vertex and second adjacent vertex, wherethe weighting value may be a score of the smart contract. For example,the weighting value of the first adjacent vertex may be 2/4 and theweighting value of the second adjacent vertex may be 1/6. Someembodiments may then update the symbolic AI model when it is in thesecond state based on the second simulated input in order to advance thesecond model to a terminal state, where a terminal state is one thatsatisfies a terminal state criterion. Once in a terminal state, thesymbolic AI system may update the weighting values associated with thesymbolic AI model before performing another iteration of the simulation.

Various terminal state criteria may be used. For example, a terminalstate criterion may be that there is no further state change possible.Alternatively, a terminal state criterion may be that the smart contractis cancelled. The system may then update each of the weighting valuesassociated with each of the nodes after reaching a terminal state beforeproceeding to perform another iteration. In some embodiments, thesymbolic AI system may set a status of a vertex to “failed” to simulatethe outcomes of a first entity failing to transfer a score (e.g. failureto pay) a second entity.

In some embodiments, the determination of an input may be based on thetype of conditional statement being triggered. As further discussedbelow, one or more of the conditional statements may be non-exclusivelyclassified as one or more types of norms. Example of norm types includerights norms, obligations norms, or prohibition norms. As furtherdiscussed below, norm types may also include associations as being partof a pattern, such as a permission pattern. For example, a vertex mayinclude or be otherwise associated with the label “consent or request.”By determining activities based on logical categories associated withthe conditional statements instead of specific events, predictivemodeling may be performed using globalized behavior rules withoutinterpreting each of the globalized behavior rules for each specificcontract. For example, a sequence of event may be generated a based on afirst probability distribution that approximates an obligation of afirst entity as having a 95% chance of being fulfilled and a 5% chanceof being denied and a second probability distribution that approximatesthat a second entity has a 10% chance of cancelling a smart contractbefore the first entity exercises a right to cure the failure to satisfythe obligation. Using these rules, population scores associated with thepopulation of smart contracts between a first entity and a second entitythat consist of obligations norms to pay, rights norms to cure, andrights norms to cancel may be determined without regards to the specificstructure of individual smart contracts in the population of smartcontracts.

The system may then update each of the smart contract instancesassociated with a changed norm status, as discussed further below.Furthermore, the system may then update the respective associative arrayof conditions corresponding to the set of smart contracts. In someembodiments, an associative array of conditions may include only asubset of norm conditions associated with a smart contract, where eachthe subset of norm conditions is associated with a triggerable vertex ofthe smart contract. In some embodiments, the system may firstdeduplicate the norm conditions before performing a lookup operation toincrease performance efficiency. For example, after determining that anevent has occurred, some embodiments may search through a deduplicatedarray of norm conditions. For each norm condition that the event wouldtrigger, the system may then update the one or more smart contractsassociated with the norm condition in the deduplicated array of normconditions.

Some embodiments may obtain a sequence of inputs instead of a singleinput. In some embodiments, the system may use a neural network togenerate the sequence of inputs. In some embodiments, the neural networkmay determine a state value s based on the program state data andprovide a vector of probabilities associated with for each of a set ofpossible changes in the program state. The neural network may alsodetermine a state value to estimate the expected value of the programstate after system applies the scenario to the program. In someembodiments, the neural network may use a MCTS algorithm to traverse atree representing possible future states of the smart contract from aroot state. The system may determine a next possible state s₊₁ for eachstate s by selecting a state with a low visit count, high predictedstate value, and high probability of selection. The parameters (e.g.weights, biases, etc.) of the neural network making the state valuedetermination may be represented by θ. After each iteration ending in aterminal state, the system may adjust the values θ to increase theaccuracy of the neural network's predicted state value in comparison tothe actual state value assessed whenever a terminal state is reached.Furthermore, a symbolic AI model may have a total score value, and thesystem may update the total score value based on the state value.

In some embodiments, the process 300 includes determining an outcomescore based on the updated program state data, as indicated by block336. In some embodiments, as stated in this disclosure, a set of scoresmay be associated with one or more of the outcome states. For example,an outcome of a first norm may include a transfer of currency valuesfrom a first entity to a second entity. The symbolic AI system mayrecord this score and combine it with other scores in the same iterationin order to determine a net score for that score type. For example, thesymbolic A system may record each currency change based on inputs andoutcomes in order to determine a net currency change, where a score ofthe smart contract may be the net currency change. Alternatively, or inaddition, the symbolic AI system may record scores across differentiterations to determine a multi-iteration score, as described furtherbelow. Example outcome scores may include a net amount of currencyexchanged, a net amount of computing resources consumed, a change in thetotal cryptocurrency balance for an entity, or the like.

The process 300 may execute a number of iterations of smart contractstate change simulations to determine possible outcomes and outcomescores. In some embodiments, there may be one or more criteria todetermine if an additional iteration is needed, as indicated by block340. In some embodiments, the one or more criteria may include whetheror not a pre-determined number of iterations of simulations have beenexecuted. For example, some embodiments may determine that additionaliterations are needed if the total number of executed iterations is lessthan an iteration threshold, where the iteration threshold may begreater than five iterations, greater than ten iterations, greater than100 iterations, greater than 1000 iteration, greater than one millioniterations, greater than one billion iterations, or the like.Alternatively, or in addition, the one or more criteria may includedetermining whether a specific outcome occurs. For example, the one ormore criteria may include determining whether the outcome score is lessthan zero after a terminal state is reached. If the additionaliterations are needed, operations of the process 300 may return to block320. Otherwise, operations of the process 300 may proceed to block 344.

In some embodiments, the process 300 includes determining amulti-iteration score based on the outcome scores of executediterations, as indicated by block 344. The multi-iteration score may beone of various types of scores and may include values such as a netchange in score across multiple iterations, a probability distributionparameter, a measure of central tendency across multiple iterations, ameasure of dispersion, or a measure of kurtosis. For example, the systemmay use a first outcome score from a first iteration, a second outcomescore from a second iteration, or additional outcome scores fromadditional iterations to determine an average outcome score. The systemmay determine additional multi-iteration scores in the form ofprobability distribution parameters to determine a probabilitydistribution. As used herein, a measure of kurtosis value may becorrelated with a ratio of a first value and a second value, wherein thefirst value is based on a measure of central tendency, and wherein thesecond value is based on a measure of dispersion. For example, themeasure of kurtosis may equal to μ⁴/σ⁴, where p may be a fourth centralmoment of a probability distribution and a may be a standard deviationof the probability distribution.

In some embodiments, the multi-iteration score may be used to provideone or more predictions using Bayesian inference methods. In someembodiments, the multi-iteration score may be used to generate aprobability distribution for the probability that a particular event orevent type occurred based on a score, such as a change in currency valueor an amount of computing resources consumed. For example, the systemmay calculate a mean average cryptocurrency amount determined acrossmultiple iterations as a first multi-iteration score and a standarddeviation of the cryptocurrency amount as the second multi-iterationscore while tracking the number of payment delays associated with therespective cryptocurrency amounts. The system may then use the first andsecond multi-iteration scores to generate a gaussian distribution, wherethe system may use the gaussian distribution to perform Bayesianinferences in order to determine a probability that a payment delayoccurred after obtaining the value of a new cryptocurrency amount.

In some embodiments, the multi-iteration score may be a weight, bias, orother parameter of a neural network. For example, some embodiments mayuse a set of multi-iteration scores as weights of a neural network,where the training inputs of the neural network may be outcome scoresand the training outputs of the neural network may be events, indicatorsrepresenting activated outcome subroutines, or activated patterns. Oncetrained, the neural network may determine the probability of events,triggered conditional statements, or triggered patterns based onobserved scores. In some embodiments, the parameters of the neuralnetwork may be transferred to other neural networks for furthertraining. For example, a first neural network may be trained using theoutcome scores as inputs and sets of events as outputs, and the weightsand biases of the training may be transmitted to a second neural networkfor further training. The second neural network may then be used toindicate whether a particular event had a sufficiently high possibilityof occurring based on a score or score change. In addition, themulti-iteration score may include outputs of a convolutional neuralnetwork, which may be used to determine behavior patterns acrossmultiple smart contracts.

In some embodiments, the symbolic AI system may use a fuzzy logic methodto predict the occurrence of an event based on the outcomes of a smartcontract. A fuzzy logic method may include fuzzifying inputs by usingone or more membership functions to determine a set of scalar values foreach of a set of inputs, where the set of scalar values indicate thedegree of membership of the inputs of a set of labels for each of theinputs of a smart contract being simulated by a symbolic AI system. Forexample, the system may use a membership function to determine apercentage values between 0% and 100% for a set of labels such as“profitable,” “risky,” or the like. The percentage values may indicate,for each of the smart contracts, a degree of membership to each of thelabels. The symbolic A system may then determine an fuzzified outcomescore based on the set of fuzzified data by first using a set of rulesin combination with an inference engine to determines the degree ofmatch associated with the fuzzy input and determine which of the set ofrules to implement. As used herein, an inference engine may be a systemthat applies a set of pre-defined rules. For example, an inferenceengine may include a set of “if-then” expressions that providedresponses to particular inputs. By using the inference engine incombination with the set of rules, the fuzzified outcome score mayprovide an indication of a broader label for the smart contract, such as“unconventional,” “risk too high,” or the like. In some embodiments, thesymbolic AI system may defuzzify the fuzzified outcome score usingvarious methods such as centroid of area method, bisector of areamethod, mean of maximum method, or the like. The defuzzifying processmay result in a defuzzified outcome score that may also be used todetermine a label.

In some embodiments, each of the scenarios may have an associatedscenario weight, where the associated scenario may be a numeric valuerepresenting a normalized or nonnormalized probability of occurrence.For example, a smart contract may be processed based one of threepossible scenarios, where the first scenario may have a weighting valueequal to 0.5, the second scenario may have a weighting value equal to0.35, and the third scenario may have a weighting value equal to 0.15.The system may use the associated scenario weights when determining amulti-iteration score. For example, if the first, second, and thirdscenarios results in allocating, respectively, 100, −10, or −100computing resource units to a first entity, the system may determinethat the expectation resource units allocated to the first entity isequal to 31.5 computing resource units and use the expectation resourceunits as the allocation value. While the above described using a scalarvalue as a weighting value, some embodiments may instead use aprobability distribution as an associated scenario weight for each ofthe scenarios and determine the weighting value.

In some embodiments, the system may determine if data from an additionalsmart contract is to be processed, as indicated by block 350. Asdiscussed in this disclosure, the process 300 may execute a number ofsimulations of different smart contracts to simulate possible outcomesand score changes. In some embodiments, each of the set of selectedsmart contracts may be simulated using a symbolic AI simulator.Furthermore, each of the set smart contracts may use the same set ofweights/probability values to determine unique scenarios. For example,using the same set of weights corresponding to different combinations ofavailable vertices, the system may determine a first scenario for afirst symbolic AI model and a second scenario for a second symbolic AImodel, where the first and second symbolic AI models have directedgraphs that are different from each other. In some embodiments, the sameweights may be used because the plurality of symbolic AI models mayinclude vertices based on the same set of statuses and same set oflogical categories. If the additional iterations are needed, operationsof the process 300 may return to block 308. Otherwise, operations of theprocess 300 may proceed to block 354.

In some embodiments, the process 300 includes determining amulti-protocol score based on the outcome scores across multiple smartcontracts, as indicated by block 354. A multi-protocol score may be anyscore that is determined based on a plurality of outcomes fromsimulating different smart contracts, where the plurality of outcomesmay include either or both multi-iteration scores or scores determinedafter a single iteration. In some embodiments, the multi-protocol scoremay be determined by determining a population of scores associated witha given entity. For example, a population of scores may be a populationof expected income across a population of 500 instantiated smartcontracts. The multi-protocol score may be a total income value, averageincome value, kurtosis income value, or the like.

In some embodiments, one or more methods to determine a multi-iterationscore may be used to determine a multi-protocol score. For example, useof fuzzy logic, Bayesian inference, or neural networks may be used topredict multi-protocol scores. For example, some embodiments may use afirst set of multi-iteration scores from a plurality of smart contractsimulations as inputs and a second set of multi-iteration scores fromthe same plurality of smart contract simulations as outputs whentraining a neural network, where a set of multi-protocol score may beone or more the parameters of the trained neural network. For example,some embodiments may include a neural network trained to predict theprobability that a specific type of smart contract was used based onmulti-iteration scores such as an average payment duration and anaverage payment amount.

In some embodiments, multiple multi-protocol scores may be used todetermine risk between a first entity and a second entity. For example,operations of the process 300 may be performed to determine a list ofsmart contracts shared by a first entity and a second entity and predictpossible risks to the first entity in scenarios resulting from theincapability of the second entity to fulfill one or more norms in thelist of smart contracts. In some embodiments, the risk posed to a firstentity by a second entity may include considerations for intermediaterelationships. For example, a first entity may be owed multiple amountsfrom a plurality of entities other than a second entity, and a secondentity may owe multiple amounts to the plurality of entities. In someembodiments, a risk associated with the total amount of a score value tobe collected by the first entity from the plurality of entities may beassessed based on the risk of the second entity failing to fulfill oneor more obligations to transfer score values to one or more of theplurality of entities. While the relationship between the first entityand the second entity may be difficult to determine using conventionalsmart contract systems if no explicit privity relations are listed inthe smart contracts, the symbolic AI models described in this disclosureallow these relationships to be determined by searching through entitylists or crawling through one or more privity graphs.

FIGS. 4-9 below show a set of directed graphs that represent examples ofprogram state of a smart contract or a simulation of a smart contract.Each vertex of the directed graph may represent conditional statementsthat encode or are otherwise associated with norm conditions and outcomesubroutines that may be executed when a norm condition is satisfied.Each directed graph edge of the directed graph may represent arelationship between different conditional statements. For example, thetail vertex of a directed graph edge may represent a norm vertex that,if triggered, will activate the respective head vertex of the directedgraph edge. As used in this disclosure, the direction of a directedgraph edge points from the tail vertex of the directed graph edge to thehead vertex of the directed graph edge. Furthermore, the direction ofthe of the directed graph edge may indicate that the respective headvertex to which the directed graph edge points is made triggerable basedon a triggering of the respective tail vertex. In some embodiments, anorm vertex may be triggered if the trigger direction is the same as thedirected graph edge direction for each directed graph edge. In someembodiments, the direction of a directed graph edge associated with normcondition may be used to categorize a norm or norm vertex.

Figure show a computer system for operating one or more symbolic AImodels, in accordance with some embodiments of the present techniques.As shown in FIG. 4, a system 400 may include computer system 402, firstentity system 404, second entity system 406 or other components. Thecomputer system 402 may include a processor 412 and a local memory 416,or other components. Each of the first entity system 404 or secondentity system 406 may include any type of mobile computing device, fixedcomputing device, or other electronic device. In some embodiments, thefirst entity 404 may perform transactions with the second entity 406 bysending messages via the network 450 to the computer system 402. In someembodiments, the computer system 402 may execute one or moreapplications using one or more symbolic AI models with a processor 412.In addition, the computer system 402 may be used to perform one or moreof the operations described in this disclosure for the process 100 orthe process 300. Parameters, variables, and other values used by asymbolic AI model or provided by the symbolic A model may be retrievedor stored in the local memory 416. In some embodiments, parameters,variables, or other values used or provided by the computer system 402,entity systems 404-406, or other systems may be sent to or retrievedfrom the remote data storage 444 via the network 450.

FIG. 5 includes a set of directed graphs representing triggered normsand their consequent norms, in accordance with some embodiments of thepresent techniques. The table 500 shows various triggered vertices andtheir respective consequent vertices in the form of directed graphs. Insome embodiments, the system may include categories for norms of a smartcontract based on a deontic logic model, where the categories mayinclude obligation norms, rights norms, or prohibition norms. Inaddition to various contract-specific ramifications of these categories,norms within each category may share a common set of traits with respectto their transiency and possible outcomes. As shown in table 500, therelationship between a triggered norm and its consequent norms may berepresented as a directed graph, where each of the norms may berepresented by a vertex of the directed graph and where each triggeringevent may be used to as a label associated with a graph edge.

Box 510 includes a directed graph representing a smart contract state(or simulation of the smart contract state) after an event satisfying anorm condition of the obligation norm represented by the norm vertex511. As shown in box 510, after a determination that the norm conditionP associated with the norm vertex 511 is satisfied by an event(indicated by the directed graph edge 512), the system may generate anadjacent vertex 513 indicating that the norm vertex 511 is satisfied,where a norm status of the adjacent vertex 513 may be set as “terminal”to indicate that the adjacent vertex is terminal. In some embodiments, adetermination that the state of the smart contract or simulation thereofis terminal may be made if a vertex of the smart contract or simulationthereof indicated to be terminal. In some embodiments, instead ofgenerating the adjacent vertex 513, the system may update a norm statusassociated with the norm vertex 511 to indicate that the norm vertex 511is satisfied. For example, the system may update a norm vertexassociated with the obligation norm by setting a norm status associatedwith the norm vertex to “satisfied,” “terminal,” or some other indicatorthat the obligation norm has been satisfied by an event. In someembodiments, updating a norm vertex associated with the obligation normmay be represented by the statement 6, where

represents a result of the norm condition associated with the obligationnorm “OP” being satisfied, and S represents the generation of a normvertex indicating that the conditions of the obligation norm have beensatisfied:

$\begin{matrix}{{{In}\mspace{14mu} {OP}}\overset{\mspace{11mu} P\mspace{11mu}}{\rightarrow}S} & (6)\end{matrix}$

As shown in box 520, an norm condition P may end up not satisfying anorm condition associated with the norm vertex 521 after satisfying acondition expiration threshold, where the norm vertex 521 is associatedwith an obligation norm. In response, the system may update the normvertex 521 by setting a norm status associated with the norm vertex 521to “failed” or some other indicator that the norm condition associatedwith the norm vertex 521 has been not satisfied. For example, an eventmay indicate that a condition expiration threshold has been satisfiedwithout an obligation norm condition being satisfied. In response, thesystem may generate or otherwise set as triggerable the set ofconsequent norms associated with adjacent vertices 523, where therelationship between a failure to satisfy a norm condition P of the normvertex 521 and the adjacent vertices 523 is indicated by the directedgraph edges 522. In some embodiments, the generation of the adjacentvertices may be represented by the statement 7, where

indicates that the instructions to the right of the symbol

are to be performed if a norm condition “OP” is not satisfied, and theinstructions represented by the symbolic combination ∧_(i)X_(i)Q_(i)represents the generation or activation of the consequent norms thatresult from the failure of OP:

$\begin{matrix}{{OP}\overset{\mspace{11mu} {P}\mspace{14mu}}{\rightarrow}{\Lambda_{i}X_{i}Q_{i}}} & (7)\end{matrix}$

In some embodiments, in response to an event satisfying a norm conditionof a rights norm, the system may update a norm vertex associated withthe rights norm by setting a norm status associated with the norm vertexto “exercised” or some other indicator that the rights norm has beentriggered based on an event. For example, as shown in box 530, inresponse to an event satisfying a norm condition associated a rightsnorm represented by the norm vertex 531, the system may update a normvertex associated with the norm vertex 531 by setting a norm statusassociated with the norm vertex 531 to “exercised” or some otherindicator that the rights norm has been exercised. In response, thesystem may generate or otherwise set as triggerable the set ofconsequent norms associated with adjacent vertices 533, where therelationship between satisfying a norm condition P associated with thenorm vertex 531 and the set of consequent norms associated with adjacentvertices 533 is indicated by the directed graph edges 532. Furthermore,in some embodiments, a rights norm may be contrasted with an obligationnorm by allowing a rights norm to remain triggerable after triggering.This may be implemented by further generating or otherwise setting astriggerable the rights norm associated with the rights norm vertex 534.In some embodiments, a rights norm may expire after use. For example,some embodiments may not generate the rights norm vertex 534 aftertriggering the norm vertex 531. In some embodiments, the operationdescribed above may be represented by statement 8 below, where theresult of triggering a rights norm RP₁ by satisfying the norm conditionP may result in a conjunction of newly-triggerable consequent norms∧_(i)X_(i)Q_(i) and a rights norm RP₂ that is identical to the rightsnorm RP₁, where ∧ represents a mathematical conjunctive operation:

$\begin{matrix}{{RP_{1}}\overset{\mspace{11mu} P\mspace{11mu}}{\rightarrow}{\Lambda_{i}X_{i}Q_{i}\Lambda RP_{2}}} & (8)\end{matrix}$

In some embodiments, in response to an event satisfying the normcondition of a “prohibition” norm, the system may update a norm vertexassociated with the “prohibition” norm by setting a norm statusassociated with the norm vertex to “violated” or some other indicatorthat the “prohibitions” norm has been triggered based on an event. Forexample, as shown in box 550, an event may satisfy a norm condition Passociated with the prohibition norm represented by a norm vertex 551.In response, the system may update the norm vertex 551 by setting a normstatus associated with the norm vertex 551 to “violated” or some otherindicator that the associated prohibitions norm condition has beensatisfied. In response, the system may generate or otherwise set astriggerable the set of consequent norms associated with adjacentvertices 553, where the relationship between satisfying a norm conditionP associated with the norm vertex 551 and the set of consequent normsassociated with adjacent vertices 553 is indicated by the directed graphedges 552. Furthermore, in some embodiments, a prohibitions norm may becontrasted with an obligation norm by allowing a prohibitions norm tosurvive triggering. In addition, in some embodiments, triggering aprohibitions norm may result in the system decreasing a valuerepresenting the state of the smart contract. This may be implemented byfurther generating or otherwise setting as triggerable the prohibitionsnorm associated with the prohibitions norm vertex 554 after triggeringthe norm vertex 551. In some embodiments, the operation described abovemay be represented by statement 9 below, where the result of triggeringa prohibition norm PP₁ by satisfying the norm condition P may result ina conjunction of newly-triggerable consequent norms ∧_(i)X_(i)Q_(i) anda prohibition norm PP₂ that is identical to the prohibition norm PP₁,where A represents a mathematical conjunctive operation:

$\begin{matrix}{{PP_{1}}\overset{\mspace{11mu} P\mspace{11mu}}{\rightarrow}{\Lambda_{i}X_{i}Q_{i}\Lambda PP_{2}}} & (9)\end{matrix}$

FIG. 6 includes a set of directed graphs representing possiblecancelling relationships and possible permissive relationships betweennorms, in accordance with some embodiments of the present techniques.The table 600 includes a left column that includes a directed graph 610representing an initial state and a directed graph 620 that represents afirst possible outcome state of the initial state and a directed graph630 that represents a second possible outcome state of the initialstate. In some embodiments, a norm condition may be a cancellationcondition, where satisfying a cancellation condition results in thecancellation of one or more norms. Cancelling a norm may includedeactivating a norm, deleting the norm, deleting graph edges to thenorm, or otherwise or otherwise setting the norm as not triggerable. Forexample, an obligations norm may include a cancellation outcomesubroutine, where triggering the obligations norm may result in thecancellation of one or more norms adjacent to the obligations norm. Insome embodiments, the effect of satisfying a cancellation norm may berepresented by statement 10 below, where XP may represent an obligationsnorm,

may indicate that the event which triggers the norm XP occurs when thenorm condition P is either satisfied or failed, ∧_(i)X_(i)Q_(i)represents the set of consequent norms that are set to be triggerablebased on the event triggering XP, and X_(j)U_(j) may represent the setof consequent norms that cancelled based on event triggering XP:

$\begin{matrix}{{XP}\overset{\mspace{11mu} {P.{P}}\mspace{14mu}}{\rightarrow}{\Lambda_{i}X_{i}Q_{i}\Lambda \Lambda_{i}{{X_{j}U_{j}}}}} & (10)\end{matrix}$

As shown by statement 10 above, one or more norms may be cancelled. Insome embodiments, a cancellation may be implemented as an inactive graphedge between the norm XP and the norms X_(j)U_(j), where the graph edgerepresenting the conditional relationship between the norm XP and thenorms X_(j)U_(j) are directed towards the norm XP. In some embodiments,the cancellation of a norm may be implemented by setting an indicator toindicate that a norm or condition associated with the cancelled norm isno longer triggerable.

The directed graph 610 may represent a state of a start contract and mayinclude the first vertex 611, second vertex 613, third vertex 617, andfourth vertex 619, each of which are associated with a norm of a smartcontract. The directed graph 610 also depicts a mutual cancellationrelationship between the norm associated with the second vertex 613 andthe third vertex 617 represented by the XQ1-XQ2 graph edge 614, where amutual cancellation relationship of a pair of norm vertices may includea cancellation of one norm vertex of the pair upon triggering of theother norm vertex of the pair. The directed graph 610 also depicts aunidirectional cancellation relationship between the norm associatedwith the fourth vertex 619 and the third vertex 617 as represented bythe XP2-XQ2 graph edge 618. In some embodiments, satisfying or otherwisetriggering the norm associated with the third vertex 617 may instantiatethe RZ-XQ2 graph edge 618 and cancel the fourth vertex 619. In someembodiments, each of vertices and graph edges shown in FIG. 5 may berepresented using a protocol simulation program. For example, the firstvertex 611 may be modeled in a simulation program and may be associatedwith a conditional statement of a smart contract.

In some embodiments, the state represented by the directed graph 610 mayadvance to the state represented by the directed graph 620. The staterepresented by the directed graph 620 may be achieved by triggering thenorm associated with the second vertex 613, which may result in thecancellation of the norm associated with the third vertex 617.Furthermore, as illustrated by the directed graph 620, triggering thenorm associated with the second vertex 613 may also result in theactivation of fifth vertex 621 and sixth vertex 623. In addition,triggering the norm associated with the third vertex 617 may result inthe cancellation of the norm associated with the fourth vertex 619.Furthermore, as illustrated by the directed graph 630, triggering thenorm associated with the third vertex 617 may also result in theactivation of a seventh vertex 631 and eighth vertex 633. Each of thesetriggering behaviors may be implemented directly by a smart contract.

In some embodiments, the triggering relationship described in thisdisclosure may be modeled using a symbolic AI system that may keep trackof any scores associated with events that trigger the norms and theoutcomes of triggering the norms. For example, a first probability valuemay be assigned to the state represented by the directed graph 620 and asecond probability value may be assigned to the state represented by thedirected graph 630 during a simulation of the smart contract. Thesymbolic AI system may use the first and second probability values toadvance the state represented by either the directed graph 620 or thedirected graph 630 over multiple iterations to compute a multi-iterationscore using the methods described in this disclosure. For example, ifthe first probability value is 20% and the second probability value is80%, and a first score represented by the directed graph 620 is equal to100 cryptocurrency units and a second score represented by the directedgraph 630 is equal to 1000 cryptocurrency units, a multi-iteration scoremay be equal to 820 cryptocurrency units.

The right column of table 600 includes a directed graph 550, which mayrepresent an initial state of a smart contract (or simulation thereof).The right column of table 600 also includes a directed graph 560 thatrepresents a first possible outcome state of the initial state and adirected graph 570 that represents a subsequent possible outcome stateof the first possible outcome state. The initial state represented bythe directed graph 550 may include a permissive condition of apermission norm, where satisfying a permissive condition may result inthe activation of one or more norms. For example, after being activated,a rights norm RP may include a set of permissions {RV_(k)} that aretriggered after satisfying an norm condition associated with the rightsnorm RP, where the rights norm RP may also be described as a permissionnorm. Triggering the set of permissions {RV_(k)} may either set the normXP to be triggerable or otherwise prevent an outcome subroutine of thenorm XP from being executed until the set of permissions {RV_(k)} aretriggered. This relationship may be represented by statement 11 below,where XP may represent an obligations norm, RV_(k) represents thepermissions that must be triggered before XP may be triggered,

may indicate that the event which triggers the norm XP occurs when thenorm condition P is either satisfied or failed, ∧_(i)X_(i)Q_(i)represents the set of consequent norms that are set to be triggerablebased on the event triggering XP after the permissions RV_(k) aretriggered, and X_(j)U_(j) may represent the set of consequent norms thatcancelled based on event triggering XP after the permissions RV_(k) aretriggered:

$\begin{matrix}{{XP}{{{RV}_{k}\overset{\mspace{11mu} {P.{P}}\mspace{14mu}}{\rightarrow}{\Lambda_{i}X_{i}Q_{i}\Lambda \Lambda_{j}{{X_{j}U_{j}}}}}}} & (11)\end{matrix}$

As shown by statement 11 above, XP may be set to be triggerable upontriggering of the permission RV_(k). Triggering XP after the permissionsRV_(k) are triggered results in activation of the consequent norms∧_(i)X_(i)Q_(i) and cancels the norms X_(j)U_(j). In some embodiments,the conditions needed to trigger permissions may be activated inconjunction with rights norms dependent on the permissions, and thus XPand RV_(k) may be activated as a result of triggering the same triggerednorm. In some embodiments, permission behavior may be performed by asmart contract or a simulation thereof by modifying a first status of afirst vertex and a second status of a second vertex to indicate that thefirst and second vertices are triggered, where the first vertex mayrepresent a first rights norm such as XP and the second vertex mayrepresent a permission norm such as a norm having outcome permissionsRV_(k). The smart contract, or a simulation thereof, may trigger a thirdvertex that is adjacent to the first vertex and the second vertex suchas a vertex in ∧_(i)X_(i)Q_(i) in response to the first and secondstatuses now being triggered.

The directed graph 550 may include a first vertex 551, second vertex553, third vertex 557, and fourth vertex 559. The directed graph 550also depicts a mutual cancellation relationship between the normassociated with the second vertex 553 and the third vertex 557represented by the XQ1-XQ2 graph edge 554. The directed graph 550 alsodepicts a permission relationship between the norm associated with thefourth vertex 559 and the third vertex 557 as represented by the RZ-XQ2graph edge 558, where the fourth vertex 559 may include or otherwise beassociated with permission conditions that must be satisfied in order totrigger the third vertex 557. In some embodiments, satisfying orotherwise triggering the norm associated with the fourth vertex 559 mayinstantiate the RZ-XQ2 graph edge 558 and allow the outcome subroutinesof the third vertex 557 to be executed.

In some embodiments, the program state represented by the directed graph550 may produce an outcome state represented by the directed graph 560.The outcome state represented by the directed graph 560 may be achievedby satisfying a norm condition associated with the fourth vertex 559. Insome embodiments, after the XQ1-XQ2 graph edge 554 becomes instantiated,an event satisfying a norm condition associated with the third vertex557 may result in the program state represented by the directed graph570. The directed graph 570 may represent a program state where the normassociated with the third vertex 557 is triggered, resulting in theactivation of additional norms associated with the fifth vertex 571 andsixth vertex 573.

In some embodiments, a symbolic AI system may be used to generate ascenario that includes a sequence of inputs having a first input and asecond input. The first input may advance the state represented by thedirected graph 550 to the state represented by the directed graph 560and the second input may advance the state represented by the directedgraph 560 to the state represented by the directed graph 570. Thesequence of inputs may be determined using any of the methods describedin this disclosure. For example, the sequence of inputs may bedetermined using a Monte Carlo method, a neural network, or the like.

FIG. 7 includes a set of directed graphs representing a set of possibleoutcome states based on events corresponding to the satisfaction orfailure of a set of obligations norms, in accordance with someembodiments of the present techniques. The set of directed graphs 710includes a set of three vertices 711-713, each representing anobligation norm to perform a set of related tasks. In some embodiments,the obligation norm may represent an obligation to transmit digitalassets, deliver a data payload, or perform a computation. For example,the obligation norm represented by the first vertex 711 may beassociated with an obligation for a first entity to transmit a downpayment to a second entity, where a determination that the down paymentoccurred may be based on an event message sent by the second entityconfirming that payment was delivered. The obligation norm representedby the second vertex 712 may be associated with an obligation for thesecond entity to deliver an asset to the first entity, where adetermination that the asset was delivered may be based on an eventmessage sent by the second entity confirming that the asset wasdelivered. The obligation norm represented by the third vertex 713 maybe associated with an obligation for the first entity to pay a balancevalue to the second entity.

The set of directed graphs 720 may represent a first outcome state thatmay result from the program state represented by the set of directedgraphs 710, where each of the obligation norms represented by the threevertices 711-713 are satisfied. In some embodiments, a smart contractsimulation system such as a symbolic AI system may assign a probabilityvalue to the possibility the state represented by the set of directedgraphs 710 is advanced to the outcome state represented by the set ofdirected graphs 720. For example, a symbolic AI system may assign aprobability for the outcome state represented by the set of directedgraphs 720 to be equal to 82% when starting from the state representedby the set of directed graphs 710. The symbolic AI system may thenperform a set of simulations based on this probability value using aMonte Carlo simulator.

The set of directed graphs 730 may represent a second outcome state thatmay result from the program state represented by the set of directedgraphs 710, where the first obligation is not satisfied and the time hasexceeded a condition expiration threshold associated with the firstvertex 711. As shown in the set of directed graphs 730, a failure tomeet the first obligation represented by the first vertex 711 may resultin a system generating or otherwise activating norms associated with afourth vertex 721 and a fifth vertex 722. In some embodiments, the normassociated with the fourth vertex 721 may represent a first entity'sright to cure the payment failure and the norm associated with the fifthvertex 722 may represent a second entity's right to terminate the smartcontract. The bidirectional graph edge 723 indicates that triggering oneof the pair of vertices 721-722 will cancel or otherwise render asinactive the other of the pair of vertices 721, which may indicate thatcuring a failed obligation and terminating the smart contract may bemutually exclusive outcomes. In some embodiments, a symbolic AI system(or other modeling system) may assign a probability value to thepossibility the state represented by the set of directed graphs 710 isadvanced to the outcome state represented by the set of directed graphs720. For example, the symbolic AI system may assign a probability forthe outcome state represented by the set of directed graphs 720 to beequal to 6% when performing a simulation based on the smart contractprogram state represented by the set of directed graphs 710.

In some embodiments, the state represented by the set of directed graphs730 may be advanced to the state represented by a set of directed graphs740. In some embodiments, the state represented by a set of directedgraphs 740 may be an outcome state after the norm associated with thefourth vertex 721 is triggered. As shown in the set of directed graphs740, triggering the norm associated with the fourth vertex 721 mayresult in cancelling the norm associated with fifth vertex 722. In someembodiments, a symbolic AI system may use probability value representingthe probability of the state represented by the set of directed graphs730 advancing to the state represented by a set of directed graphs 740.For example, a symbolic AI system may use 50% as the probability thatthe state represented by the set of directed graphs 730 advances to thestate represented by a set of directed graphs 740. If the probability ofthe state represented by the set of directed graphs 720 advancing to thestate represented by a set of directed graphs 730 is equal to 6%, thiswould mean that the probability of the state represented by the set ofdirected graphs 710 advancing to the state represented by a set ofdirected graphs 740 is equal to 3% by applying the multiplication rulefor the probability of independent events.

In some embodiments, the state represented by the set of directed graphs730 may be advanced to the state represented by a set of directed graphs750. In some embodiments, the state represented by a set of directedgraphs 750 may be an outcome state after the norm associated with thefifth vertex 722 is triggered. As shown in the set of directed graphs750, triggering the norm associated with the fifth vertex 722 may resultin cancelling the norm associated with second vertex 712, third vertex713, and fourth vertex 721. In some embodiments, a symbolic AI systemmay assign a probability value to the possibility of a smart contractstate being in the outcome state represented by the set of directedgraphs 750 when starting from the program state represented by the setof directed graphs 730. In some embodiments, the probability valuesassociated with each state may be updated after each iteration in a setof simulated iterations using one or more of the methods in thisdisclosure. For example, some embodiments may apply a MCTS method toexplore the program states represented by the sets of directed graphs710, 720, 730, and 740 across multiple iterations while keeping track ofscores for each iteration in order to determine outcome scores for eachiteration and multi-iteration scores.

FIG. 8 includes a set of directed graphs representing a set of possibleoutcome states after a condition of a second obligations norm of a setof obligations norms is not satisfied, in accordance with someembodiments of the present techniques. In some embodiments, the set ofdirected graphs 810 may represent an initial state of a smart contract.Alternatively, the set of directed graphs 810 may represent an outcomestate. For example, the program state represented by the set of directedgraphs 810 may be an outcome state of the program state represented bythe set of directed graphs 710, with an associated occurrenceprobability equal to 6%. The set of directed graphs 810 may represent afailure to satisfy a norm condition associated with the second vertex812. In some embodiments, the second vertex 812 may represent anobligation norm indicating an obligation for a second entity to deliveran asset, such as a schematic, to the first entity.

In some embodiments, the state represented by the set of directed graphs810 may be advanced to the state represented by a set of directed graphs820. In some embodiments, the state represented by a set of directedgraphs 820 may be an outcome state after the norm associated with thefifth vertex 822 is triggered. As shown in the set of directed graphs820, triggering the norm associated with the fifth vertex 822 may resultin cancelling the norm associated with sixth vertex 823. In someembodiments, the fifth vertex 822 may represent a first entity's rightto terminate the order and obtain a refund. This outcome may berepresented by the eighth vertex 831, which may represent an obligationnorm indicating that the second entity has an obligation to pay thefirst entity, and that this obligation may either be satisfied orfailed, as indicated by vertices 841 and 842, respectively.

In some embodiments, the state represented by the set of directed graphs810 may be advanced to the state represented by a set of directed graphs830. In some embodiments, the state represented by a set of directedgraphs 820 may be an outcome state after the norm associated with thesixth vertex 823 is triggered. As shown in the set of directed graphs830, triggering the norm associated with the sixth vertex 823 may resultin cancelling the norm associated with sixth vertex 823. In someembodiments, the sixth vertex 823 may represent a first entity's rightto cure the failure to satisfy the norm represented by the second vertex812. This outcome may be represented by the ninth vertex 832, which mayrepresent an obligation norm indicating that the second entity has anobligation to deliver an asset to the first entity, and that thisobligation may either be satisfied or failed, as indicated by vertices843 and 844, respectively.

In some embodiments, a symbolic AI system may assign a probability valueto the possibility of a smart contract state being in the outcome staterepresented by the set of directed graphs 820 or set of directed graphs830 when starting from the program state represented by the set ofdirected graphs 810. For example, a symbolic A system may determine thatthe probability that the outcome state represented by the set ofdirected graphs 820 is equal to 40%. Similarly, the symbolic AI systemmay determine that the probability that the outcome state represented bythe set of directed graphs 830 is equal to 60%. In some embodiments, thesymbolic AI system may use a Bayesian inference to determine if anobligation norm was failed was failed based on a probabilitydistribution computed from the scores associated with program statessuch as those states represented by the sets of directed graphs 820 or830. For example, the symbolic A system may acquire a new score valueand, based on the score value, predict whether an obligation representedby the second vertex 812 was failed.

FIG. 9 includes a set of directed graphs representing a set of possibleoutcome states after a condition of a third obligations norm of a set ofobligations norms is not satisfied, in accordance with some embodimentsof the present techniques. In some embodiments, the set of directedgraphs 910 may represent an initial state of a smart contract.Alternatively, the set of directed graphs 910 may represent an outcomestate. For example, the program state represented by the set of directedgraphs 910 may be an outcome state of the program state represented bythe set of directed graphs 810, with an associated occurrenceprobability equal to 6%. The set of directed graphs 910 may represent afailure to satisfy a norm condition associated with the third vertex913. In some embodiments, the third vertex 913 may represent anobligation norm indicating an obligation for a first entity to pay abalance value to the second entity. Triggering the norm associated withthird vertex 913 by failing to satisfy an associated obligationcondition may result in activating norms associated with a sixth vertex923 and a seventh vertex 924. In some embodiments, the norm associatedwith the sixth vertex 923 may represent a first entity's right to curethe payment failure and the norm associated with the seventh vertex 924may represent a second entity's right to declare a breach and flag thefirst entity for further action (e.g. initiate arbitration, incur areputation score decrease, or the like).

In some embodiments, the state represented by the set of directed graphs910 may be advanced to the state represented by a set of directed graphs920. In some embodiments, the state represented by a set of directedgraphs 920 may be an outcome state after the norm associated with thesixth vertex 923 is triggered. In some embodiments, the norm associatedwith the sixth vertex 923 may represent a first entity's right to curethe payment failure, and thus triggering the rights norm associated withthe sixth vertex 923 may represent a first entity's right to cure thefailure. As indicated by the satisfaction vertex 931, curing the paymentfailure may end all outstanding obligations of the smart contract.

In some embodiments, the state represented by the set of directed graphs910 may be advanced to the state represented by a set of directed graphs930. In some embodiments, the state represented by a set of directedgraphs 930 may be an outcome state after the norm associated with theseventh vertex 924 is triggered. In some embodiments, the normassociated with the seventh vertex 924 may represent a second entity'sright to declare a breach, and thus triggering the rights normassociated with the seventh vertex 924 may represent a second entity'sdeclaration of contract breach. This may result in the activation of thefailure vertex 932, which may include outcome subroutines that sends amessage indicating that the smart contract is in breach to a third partyor sends instructions to an API of another application.

FIG. 10 includes a set of directed graphs representing a pair ofpossible outcome states after a condition of a fourth obligations normof a set of obligations norms is not satisfied, in accordance with someembodiments of the present techniques. FIG. 10 includes a directed graph1010 representing a first program state of a smart contract or asymbolic A simulation thereof. The program state represented by thedirected graph 1010 may be changed to the program state represented by adirected graph 1020. Alternatively, the program state represented by thedirected graph 1010 may be changed to the program state represented by adirected graph 1030. The directed graph 1010 includes a first vertex1011 that may represent an obligations norm. In some embodiments, thefirst vertex 1011 may represent an obligation norm reflecting anobligation to pay by the time a condition expiration threshold issatisfied. If the obligation to pay is failed, the obligation normassociated with the first vertex 1011 may be triggered and the rightsnorms associated with the second vertex 1012 and the third vertex 1013may be activated. The second vertex 1012 may represent a rights norm tocure the failure to satisfy the obligations norm represented by thefirst vertex 1011, and the third vertex 1013 may represent a rights normto accelerate the payments the smart contract. The directed graph 1010also includes a pair of vertices 1014-1015 representing futureobligations to pay, where exercising the rights norm represented by thethird vertex 1013 may cancel the future obligations to pay.

In some embodiments, the state represented by the directed graph 1010may be advanced to the state represented by the directed graph 1020. Insome embodiments, the state represented by the directed graph 1020 maybe an outcome state after the norm associated with the second vertex1012 is triggered. In some embodiments, the norm associated with thesecond vertex 1012 may represent a right to cure the failure to satisfythe norm condition associated with the first vertex 1011. As indicatedby the directed graph 1020, exercising the rights norm associated withthe second vertex 1012 may satisfy the norm and activate the vertex1023, which may indicate that the rights norm associated with the secondvertex 1012 has been satisfied.

In some embodiments, the state represented by the directed graph 1010may be advanced to the state represented by the directed graph 1030. Insome embodiments, the state represented by the directed graph 1030 maybe an outcome state after the norm associated with the third vertex 1013is triggered. In some embodiments, the rights norm associated with thethird vertex 1013 may represent a right to accelerate payment.Triggering the rights norm associated with the third vertex 1013 maycancel the rights norm associated with the second vertex 1012. Inaddition, triggering the rights norm associated with the third vertex1013 may also cancel the obligation norms associated with the vertices1014-1015. Triggering the rights norm associated with the third vertex1013 may cause the system to activate a new obligation norm associatedwith the fourth vertex 1031. In some embodiments, the new obligationnorm may include norm conditions to determine whether a first entitytransmits a payment amount to the second entity. For example, the newobligation norm may determine whether the first entity transmitted theentirety of a principal payment of a loan to the second entity. Theobligation norm associated with the fourth vertex 1031 may be associatedto a satisfaction norm represented by a fifth vertex 1041 or a failurenorm represented by a sixth vertex 1042.

In some embodiments, advancement of the state represented by thedirected graph 1010 to the state represented by the directed graph 1020or the state represented by the directed graph 1030 may be simulatedusing a symbolic AI system. For example, the state represented by thedirected graph 1010 may be copied into a symbolic A model, where boththe conditional statements associated with the nodes and of the directedgraph the edges connecting the nodes of the directed graph may becopied. A symbolic AI system may then simulate state changes using thesymbolic AI model to determine an expected value for a smart contractthat has already reached the state represented by the directed graph1010, where the expected value may be a multi-iteration score.

In some embodiments, each of the smart contracts represented by thedirected graphs 610, 650, 710, and 1010 may be analyzed using a symbolicAI system to determine one or more multi-protocol scores. For example,each of the smart contracts represented by the directed graphs 610, 650,710, and 1010 may be analyzed to produce multi-iteration scores such asaverage scores for each smart contract and a kurtosis value of expectedscores. In some embodiments, the analysis may use the same rules togovern the behavior entities in the smart contract by basing the ruleson logic types and vertex statuses instead of the contexts of specificagreements. For example, each smart contract simulation may be simulatedwith a set of rules that include a rule that the probability that arights norm to cure is triggered instead of a rights norm to acceleratebeing triggered is equal to 90%. The multi-iteration scores may then befurther analyzed to determine a multi-protocol score. For example, basedon a multi-iteration score representing a risk score associated witheach of the smart contracts, the total exposed risk of a first entitywith respect to a second entity may be determined, where the totalexposed risk may be a multi-protocol score.

FIG. 11 is a block diagram illustrating an example of a tamper-evidentdata store that may used to render program state tamper-evident andperform the operations in this disclosure, in accordance with someembodiments of the present techniques. In some embodiments, thetamper-evident data store may be a distributed ledger, such as ablockchain (or other distributed ledger) of one of the blockchain-basedcomputing platforms described in this disclosure. FIG. 11 depict twoblocks in a blockchain, and also depicts tries of cryptographic hashpointers having root hashes stored in the two blocks. The illustratedarrows may represent pointers (e.g., cryptographic hash). For example,the arrow 1103 may represent a pointer from a later block to block 1104that joints the two blocks together. In some embodiments, blocks may beconsecutive. Alternatively, the data from the use of a smart contractmay skip several blocks between uses of the smart contract. As shown inFIG. 11, a tamper-evident data store 1102 may include a linked list ofblocks that includes the block 1104 and other blocks, where the linkedlist of blocks may be connected by cryptographic hash pointers.

In some embodiments, a directed acyclic graph of cryptographic hashpointers may be used to represent the tamper-evident data store 1102.Some or all of the nodes of the directed acyclic graph may be used toform a skip list or linked list, such as the node corresponding to orotherwise representing as block 1104. In some embodiments, each blockrepresented by a node of this list may include multiple values ascontent. For example, each respective block may include a timestamp ofcreation 1106, a cryptographic hash of content of the previous nodepointed to by an edge connecting those nodes 1108, a state root value1110 for a trie of cryptographic hash values that may be referred to asa state trie 1118, a cryptographic hash 1112 that is a root value of areceipt trie 1124 of cryptographic hash values referred to as a receipttrie, and a cryptographic hash value 1114 that is a root value of a trieof cryptographic hash values referred to as a transaction trie 1122. Insome embodiments, the block 1104 may be connected to a plurality oftries (e.g., three or more tries) via cryptographic hash pointers. Forexample, the block 1104 may be connected to Merkle roots (or otherroots) of the plurality of tries of cryptographic hash values.

In some embodiments, the state trie 1118 may include multiple levels ofcryptographic hash pointers that expand from a root to leaf nodesthrough 2 or more (e.g. 3, 11, 5, 6, etc.) hierarchical levels ofbranching. In some embodiments, an account address of a smart contractor instance of invocation thereof may correspond to a leaf nodes, wherethe smart contract may be an instance of the smart contract described inthe process 110. In some embodiments, leaf nodes or paths to the leafnodes of the state trie 1118 may include the fields in the accountobject 1126. The address may be a smart contract address or instance ofinvocation of the smart contract, the nonce value may be a count of thetimes that the smart contract was invoked, the code hash value may be orotherwise include a cryptographic hash of a bytecode representation ofthe smart contract 1130, the storage hash may be a root (e.g. Merkleroot) of a trie of cryptographic hash pointers 1120. In someembodiments, the trie of cryptographic hash pointers 1120 may storekey-value pairs encoding a transient program state of the smart contractthat changes or is not needed between invocations of the smart contract.In some embodiments, the fields of the account object 1126 may include apredecessor pointer that points to a previous entry of an earlier statetrie corresponding to a previous invocation of the smart contract andassociated information or hashes.

FIG. 12 depicts an example logical and physical architecture of anexample of a decentralized computing platform in which a data store ofor process of this disclosure may be implemented, in accordance withsome embodiments of the present techniques. In some embodiments, theremay be no centralized authority in full control of a decentralizedcomputing platform 1200. The decentralized computing platform 1200 maybe executed by a plurality of different peer computing nodes 1202 viathe ad hoc cooperation of the peer computing nodes 1202. In someembodiments, the plurality of different peer computing nodes 1202 mayexecute on a single computing device, such as on different virtualmachines or containers of a single computing device. Alternatively, orin addition, the plurality of different computing nodes 1202 may executeon a plurality of different computing devices, where each computingdevice may execute one or more of the peer computing nodes 1202. In someembodiments, the decentralized computing platform 1200 may be apermissionless computing platform (e.g., a public computing platform),where a permissionless computing platform allows one or more variousentities having access to the program code of the peer node of thepermissionless computing platform to participate by using the peer node.

In some embodiments, the decentralized computing platform 1200 may beprivate, which may allow a peer computing node of the decentralizedcomputing platform 1200 to authenticate itself to the other computingnodes of the decentralized computing platform 1200 by sending a valuebased on a private cryptographic key, where the private cryptographickey may be associated with a permissioned tenant of the decentralizedcomputing platform 1200. While FIG. 12 shows five peer computing nodes,commercial embodiments may include more computing nodes. For example,the decentralized computing platform 1200 may include more than 10, morethan 100, or more than 1000 peer computing nodes. In some embodiments,the decentralized computing platform 1200 may include a plurality oftenants having authentication credentials, wherein a tenant havingauthentication credentials may allow authorization of its correspondingpeer nodes for participation in the decentralized platform 1200. Forexample, the plurality of tenants may include than 2, more than 12, morethan 10, more than 120, more than 100, or more than 1000 tenants. Insome embodiments, the peer computing nodes 1202 may be co-located on asingle on-premise location (e.g., being executed on a single computingdevice or at a single data center). Alternatively, the peer computingnodes 1202 may be geographically distributed. For example, the peercomputing nodes 1202 may be executing on devices at different datacenters or on devices at different sub-locations of an on-premiselocation. In some embodiments, distinct subsets of the peer nodes 1202may have distinct permissions and roles. In some cases, some of the peernodes 1202 may operate to perform the deserialization operations, graphupdate operations, or reserialization operations as described in thisdisclosure.

FIG. 13 shows an example of a computer system by which the presenttechniques may be implemented in accordance with some embodiments.Various portions of systems and methods described herein, may include orbe executed on one or more computer systems similar to computer system1300. Further, processes (such as those described for FIG. 1, 3, 14-16,21, 23-24, or 26) and modules described herein may be executed by one ormore processing systems similar to that of computer system 1300.

Computer system 1300 may include one or more processors (e.g.,processors 1310 a-1310 n) coupled to System memory 1320, an input/outputI/O device interface 1330, and a network interface 1340 via aninput/output (I/O) interface 1350. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computersystem 1300. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may include one or more microcontrollers. A processor mayreceive instructions and data from a memory (e.g., System memory 1320).Computer system 1300 may be a uni-processor system including oneprocessor (e.g., processor 1310 a), or a multi-processor systemincluding any number of suitable processors (e.g., 1310 a-1310 n).Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computer system 1300 mayinclude a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1330 may provide an interface for connection of oneor more I/O devices 1360 to computer system 1300. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1360 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1360 may be connected to computer system 1300through a wired or wireless connection. I/O devices 1360 may beconnected to computer system 1300 from a remote location. I/O devices1360 located on remote computer system, for example, may be connected tocomputer system 1300 via a network and network interface 1340.

Network interface 1340 may include a network adapter that provides forconnection of computer system 1300 to a network. Network interface may1340 may facilitate data exchange between computer system 1300 and otherdevices connected to the network. Network interface 1340 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1320 may be configured to store program instructions 1324or data 1310. Program instructions 1324 may be executable by a processor(e.g., one or more of processors 1310 a-1310 n) to implement one or moreembodiments of the present techniques. Program instructions 1324 mayinclude modules of computer program instructions for implementing one ormore techniques described herein with regard to various processingmodules. Program instructions may include a computer program (which incertain forms is known as a program, software, software application,script, or code). A computer program may be written in a programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1320 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory, computer-readable storage medium. A non-transitory,computer-readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory, computer-readable storage mediummay include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1320 may include a non-transitory, computer-readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1310 a-1310 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., System memory 1320) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). Instructions or other program code toprovide the functionality described herein may be stored on a tangible,non-transitory, computer-readable media. In some cases, the entire setof instructions may be stored concurrently on the media, or in somecases, different parts of the instructions may be stored on the samemedia at different times.

I/O interface 1350 may be configured to coordinate I/O traffic betweenprocessors 1310 a-1310 n, System memory 1320, network interface 1340,I/O devices 1360, and/or other peripheral devices. I/O interface 1350may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., System memory 1320) into a formatsuitable for use by another component (e.g., processors 1310 a-1310 n).I/O interface 1350 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1300 or multiple computer systems1300 configured to host different portions or instances of embodiments.Multiple computer systems 1300 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1300 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1300 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1300 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a GPS device, or the like.Computer system 1300 may also be connected to other devices that are notillustrated, or may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may in someembodiments be combined in fewer components or distributed in additionalcomponents. Similarly, in some embodiments, the functionality of some ofthe illustrated components may not be provided or other additionalfunctionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described in thisdisclosure. In some embodiments, instructions stored on acomputer-accessible medium separate from computer system 1300 may betransmitted to computer system 1300 via transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as a network or a wireless link. Variousembodiments may further include receiving, sending, or storinginstructions or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Accordingly, the presenttechniques may be practiced with other computer system configurations.

In some embodiments, a program state of a smart contract program encodedin a symbolic AI system may include a directed graph, where entities ofthe symbolic AI system may cause events that simulate evolving programstate by selecting vertices of the directed graph. Event-causingoperations performed by an entity to establish, maintain, or endrelationships between entities may be analyzed by an intelligent agentbeing executed by a computing system. An intelligent agent may includeany set of functions, operations, routines, or applications that mayperceive or otherwise obtain input values, refer to one or more storedparameters, and determine an outcome response from the input valuesusing the one or more stored parameters, wherein at least one routine ofthe intelligent agent allows for one or more of the set of storedparameters to be changed over time. The intelligent agent may takeadvantage of the directed graph and its associated categorizations todetermine a set of outcome program states after an event caused by afirst entity or a counterparty entity, where the events caused by thecounterparty entity are not in the control of the first entity. Usingthe set of outcome program states, the intelligent agent may respond tocounterparty-caused events.

Operations to determine outcome states of a multiparty smart contractprogram are often prevented by the difficulty of accounting forinformation asymmetry, differing goals amongst entities, andcomputational complexity. In some embodiments, an entity of a multipartysmart contract program may perform an action without having theinformation necessary for precisely valuing the action. In someembodiments, an entity of a multiparty smart contract program may havedifferent goals or strategies with respect to another entity of theprogram. Furthermore, an entity or its counterparty entities of amultiparty smart contract program may have a large number of options andvariations of those options available when causing a state-changingevent, which may increase the difficulty of decision-making operationsfor AI systems tasked with predicting the outcomes of those events. Byusing a symbolic A model that models entity transactions using adirected graph with associated vertex categories, a system may moreefficiently and accurately overcome these difficulties to determineoutcome states and their associated outcome stores.

A smart contract program implemented using a symbolic AI model mayinclude vertex categories associated with norms. The use of vertexcategories in association with the norms or norm vertices of a symbolicAI model may increase the speed and accuracy of outcome determination.The categorization of the norms or their associated directed graphvertices into a set of vertex categories such as “obligations,”“rights,” “prohibitions,” or the like may indicate outcome behaviors inresponse to their conditional statements (“conditions”) being satisfiedor not satisfied. By pre-categorizing a set of vertices of a directedgraph representing the program state of a smart contract program intoone of a set of vertex categories, the system may rapidly simulateevolving a program state into possible future states. In addition, thesystem may be free from computing burdens related to examining eachindividual condition and conditional outcome of every norm encoded in asmart contract program or parsing ad-hoc coded statements. Suchadvantages may be especially useful in many scenarios involving a graphof a symbolic A model that includes a large number of vertices, such asmore than 100 vertices or more than 1000 vertices.

FIG. 14 is a flowchart of an example of a process by which a program mayuse an intelligent agent to determine outcome program states for smartcontract programs, in accordance with some embodiments of the presenttechniques. In some embodiments, the process 1400, process 1500, orprocess 1600 like the other processes and functionality describedherein, may be implemented by a system that includes computer codestored on a tangible, non-transitory, machine-readable medium, such thatwhen instructions of the code are executed by one or more processors,the described functionality may be effectuated.

In some embodiments, one or more operations of the process 1400 may beotherwise initiated in response to a detected change in a program stateof an application. For example, an event obtained by a symbolic AIsystem may indicate that an asset has been removed from a set of assets,which results in the change of an asset value stored in the programstate data of the symbolic AI system. In some embodiments, as furtherdescribed below, in addition to initiating one or more operations of theprocess 1400, one or more parameters of an intelligent agent associatedwith an entity may be updated based on the changed program state.Alternatively, or in addition, operations of the process 1400 may beginwithout detecting a change in the program state of the application.

In some embodiments, the process 1400 includes obtaining a currentprogram state of a symbolic AI model associated with a plurality ofentities, as indicated by block 1404. In some embodiments, the symbolicAI model may include a smart contract program, such as a smart contractdescribed above for the operation 304. The program state of the symbolicAI model may include a directed graph, such as the directed graphsdescribed above. The use of “norm” in the context the process 1400 mayinclude or be otherwise associated with a norm vertex of a directedgraph representing some or all of a program state of the symbolic Amodel. In some embodiments, the current program state may indicate anuninitialized form or otherwise un-triggered form and may include adirected graph including a single active vertex without apreviously-triggered vertex. Alternatively, the current program statemay have already progressed from an initial and may include a set ofvertices that have already been triggered. In some embodiments, thedirected graph of a current program state may have a few number ofvertices, such as one vertex, two vertices, less than five vertices,less than ten vertices, or the like. In some embodiments, the directedgraph of a current program state may have a large number of vertices,such as 100 vertices, 1000 vertices, 10,000 vertices, or the like.

In some embodiments, the process 1400 includes determining one or moreprogram states and parameters for using, training, or modifying anintelligent agent based on the current program state, as indicated byblock 1408. As further described below, an intelligent agent may includea set of programs, routines, sub-routines, or the like being executed bythe system to determine outcome program states or their associatedoutcome scores based on an input program state. In some embodiments, aprogram state used for or by an intelligent agent may be identical tothe current program state. Alternatively, a program state used in theprocess 1400 may be modified from a current program state, where somevalues of the modified program state may differ from the values of thecurrent program state. In some embodiments, the program state in theprocess 1400 may include changes in values to simulate additionalpossibilities at a current time or in the future. For example, a firstentity may be engaged with the second entity in a smart contract programto stream a computing resource to the second entity. Even if the currentprogram state indicates that the computing resource is available, theprogram state used in the process 1400 may include a program staterepresenting a scenario in which the computing resource becomescorrupted while being used by the second entity. The smart contractprogram may include a rights norm that allows the second entity toprovision additional computing resources in such an event, which mayallow this scenario to be tested. As described further below, multiplescenarios may be used to determine action values across multiplepossible scenarios. In some embodiments, probabilities or other measuresof likelihood may be assigned to scenarios. A richer set of possibleoutcome program states determined from these associated measures oflikelihood may be used to account for possible future outcomes.

As described below, a scenario may be determined based on actionsperformed by additional entities. For example, if a first smart contractprogram includes a set of obligation norms and rights norms associatedwith a first entity and a second entity, the actions of a third entitythat is not an entity listed in the first smart contract program maystill affect the program state of the first smart contract program.These effects may manifest by altering a parameter of the program state,such as increasing an amount of required material density in amanufacturing context or alter a specific state of an entity. Forexample, a failure of the third entity to transfer an asset or scorevalue to the second entity may likewise affect the program state of thefirst entity by altering one or more expected reward values associatedwith the values being transferred by the second entity. As anotherexample, if the second entity is noted to fail an obligation to thethird entity, a parameter associated with the second entity may bemodified to indicate that the second entity has a higher likelihood offailing an obligation to the first entity.

In some embodiments, the process 1400 may include categorizing a programstate value using one or more categorization operations, as indicated byblock 1412. The program state value may be a numerical value, acategorical value, a set of values, or the like. The categorized programstate may include one or more converted values that are based on the oneor more program state value. For example, a first score value in theprogram state that is equal to “30” may be converted to a categoricalvalue using a categorization operation that bins all quantitative valuesbetween 0 to 15 into the category “LOW” and all quantitative valuesgreater than 15 into the category value “HIGH,” resulting in theconversion of the program state value from 30 into the categorizedprogram state value “HIGH.” Using the one or more categorizationoperations may result in a discretized representation of a program statevalue, which may reduce the computation resources required to generatecounterparty predictions. Furthermore, an intelligent agent may accountfor categorized program states during training or prediction operationseither by applying an initial simulated event to a program state andthen modifying the program state using a categorization operation or byapplying the categorization operation to the simulated event directly.

In some embodiments, the process 1400 may include determining a set ofpossible state-changing events causable by an entity based on theprogram state, as indicated by block 1416. In some embodiments, the setof possible state-changing events causable by an entity may bedetermined based on conditions associated with a set of active verticesassociated with norms. Determining the possible state-changing eventscausable by the entity may include finding the set of vertices indicatedas active in the program state (“set of active vertices”). For example,the program state may include a set of active vertices, where the set ofactive vertices are associated with a set of norms that include anobligation norm to allocate memory, a rights norm to inspect an assetprovided in response to the allocated memory, and a prohibition norm ondeleting allocated memory. The term “active vertex” means that the normassociated with the active vertex is triggerable such that satisfying aset of conditional statements (“set of conditions’) associated with thenorm will result in the system performing one or more operationsconditional on the set of conditions being satisfied.

In some embodiments, the set of possible state-changing events causableby an entity may also include state-changing events associated withnorms that are not active in the current program state. For example, afirst norm may be a rights norm to terminate a smart contract that maybe activated if a specified type of encrypted message is sent by a thirdentity via a specific API. A current program state may be such that thespecified type of encrypted message has not been sent, but anintelligent agent may use a scenario that includes a probability thatthe specified type of encrypted message will be sent in the future. Forexample, the scenario may include that the probability of the specifiedtype of encrypted message will be sent within the next two months isequal to 0.15%. As further described below, during traversal of adirected graph of the program state that spans one or more edges, anintelligent agent may simulate the encrypted message being sent andinclude its subsequent outcome states as part of an expanded directedgraph. The outcome states and their associated outcome scores may thenbe included and considered by the intelligent when providing projectionsof outcomes.

In some embodiments, a plurality of events may trigger one or more of aset of active vertices associated with active norms. For example, afirst state-changing event may include allocating 10 gigabytes (GB) fromthe first entity to a second entity and a second state-changing eventmay include allocating 50 GB from the first entity to the second entity,where either the first state-changing event or second state-changingevent may both satisfy a same obligation norm. In some embodiments, thesystem may simulate and keep both events as separate events and, asfurther described below, determine different action values for each ofthe selected set of events, where each of the selected set of events maybe associated with an action of an entity. Alternatively, someembodiments may simplify multiple possible events that satisfy a norm toone or more categorized events, such as providing a minimum or maximumamount of allocated memory necessary to satisfy a norm and determine anaction score for the categorized event. In some embodiments, as furtherdescribed below, simulations of future program states may directlytrigger norms of a program state, where the conditions associated withtriggering the norms may be retroactively applied to determine the eventthat triggered the norm. In some embodiments, the selected set of eventsmay be determined based on probability distributions associated with theentities. For example, a first entity may include a set of entityproperties defining a gaussian distribution, where a mean memoryallocation defined by the gaussian distribution may be used to determinean amount of memory allocated in an event of the selected set of events.

While the above example describes an event to allocate memory, otherevents are possible. For example, some embodiments may include eventssuch as an allocation of a transfer rate in a data pipeline, atransmission of instructions to accelerate a payment deadline via anAPI, or transference of an asset from a first entity to a second entity.For example, in response to the determination that an active vertex isassociated with an obligation norm for a first entity to pay a minimumamount, the set of possible events may include a first possible eventcaused by the first entity transferring the minimum amount, a secondpossible event caused by the first entity to transferring 200% of theminimum amount, and a third possible event caused by the first eventtransferring a remaining balance in full. In some embodiments, asfurther described below, a learning system may be used to simulate theoccurrence of multiple events that lead to a same directed graph shapebut may result in different final program states.

In some embodiments, the process 1400 includes updating a set of actionvalues or other parameters of an intelligent agent for the set ofpossible state-changing events based on parameters of an intelligentagent or a set of vertex categories of the symbolic AI model, asindicated by block 1424. In some embodiments, the set of parameters ofan intelligent agent may include parameters defining a policy, where apolicy may include a policy function that maps a program state to anevent caused by an entity or action or a probability of the eventoccurring. In some embodiments, the policy function may be deterministicand predict the same action when presented with the same program state.Alternatively, the policy function may be stochastic, wherein a sameprogram state may result in different predicted actions based on aprobability distribution. For example, when provided a first state as aninput, the policy function may provide 25% as the possibility for thefirst state to transition to a second state and 75% as the probabilityfor the first state to transition to a third state.

In some embodiments, an action value may include a quantitative orcategorical value. An action value may be associated with an eventcaused by an entity and, as further described below, and may be used todetermine outcome states or response instructions. Alternatively, anaction value for an entity may be associated with an action of theentity, where the action may be analyzed to determine which elements ofthe action change a program state. For example, a first entity mayperform an action that includes transferring an amount to the secondentity along with a message indicating that no further transfers willoccur. In some embodiments, a system may ignore the message as having noeffect on a program state and consider the event associated with theaction as identical to the first entity transferring the amount to thesecond entity without sending an associated message. In someembodiments, updating an action value may include changing an existingvalue associated with the action value. Alternatively, updating theaction value may include determining a new action value and associatingit with the possible state-changing events causable by an entity. Insome embodiments, the action value may be modified or otherwisedetermined based on an entity property, as further described in thisdisclosure. For example, an entity property may modify the likelihood ofevents occurring, modify a reward value associated with a possibleoutcome of an action, or otherwise determine a behavior patternassociated with an entity.

In some embodiments, the parameters of the intelligent agent may includethe action values themselves. For example, the intelligent agent may usethe action values as parameters of the policy function by including anoperation to select which event is most likely to be caused by an entitybased on the action values. The policy function may select the eventfrom a set of possible events by determining which event has thegreatest action value from the action values associated with the set ofpossible actions.

In some embodiments, the action value associated with a set of possiblestate-changing events causable by an entity may be determined based on areward value associated with a change from a first program state to asecond program state caused by the state-changing event. The rewardvalue may be equal to or otherwise based on a score change of an entityand may be associated with one or more conditions of the set ofconditions. For example, if a condition to satisfy an obligation normincludes a threshold value of 10 units, where the threshold valueindicates that satisfying the obligation norm requires an allocation of10 computing instances to a first entity from a second entity, thereward value for the obligation norm or its associated directed graphvertex may be +10 for the second entity. In some embodiments, eachentity may have its own set of reward values and, correspondingly, theirown set action values based on those reward values. For example, a firstentity may have +10 as the reward value associated with a first vertexbased on a threshold value of 10 for a condition associated with thefirst vertex, and a second entity may have −20 as the reward valueassociated with the same first vertex based on the threshold value andan addition −10 as part of a transaction cost associated with the firstvertex. In some embodiments, the set of vertex categories may be used tomodify or set reward values based on their failure or satisfaction. Forexample, the system may determine that a first vertex is categorized asan obligation and, in response to a status change indicating that thefirst vertex is failed, deduct a failure penalty from the reward valueassociated with the first vertex or a child vertex of the first vertex.

In some embodiments, a reward value may be modified based on an entityproperty value associated with an entity or entity role. The entityproperty value may be used to change the reward value to a greater orlower value. An entity property may indicate an entity's characteristic,limitation, behavior pattern, or the like. For example, a first entitymay be associated with a first entity property for an entity propertycorrelated with an acceptable loss before catastrophic entity failure(“loss multiplier threshold”), where losses greater than the firstentity property value are multiplied by five (or some other numericvalue). For example, if the first entity property value for a lossmultiplier threshold is 200, losses greater than 200 will be multipliedby five, such that the reward value for a loss of “200” may be equal to“−200” for the first entity, but a reward value for a loss of “201” maybe equal to “1005.” Alternatively, or in addition, instead of applying amultiplier to a value, some embodiments may set a reward value to apre-determined value. As further discussed below, by altering rewardvalues based on one or more entity properties, entity biases,tendencies, or behavior patterns may be simulated. Alternatively, or inaddition, a probability distribution defined by one or more entityproperties may be used to modify or otherwise determine a reward value.

By using a threshold value encoded in or otherwise associated with theconditions associated with norm vertices, efficient estimation of rewardvalues may be performed without requiring additional and potentiallyerror-prone simulations of unexpected events or entity behaviors whichare unlikely to occur or whose variations are otherwise not relevant toan outcome program state or outcome score. In some embodiments, thethreshold value of a condition itself may be stored in a conditions datamodel and may be retrieved based on an identifier of a vertex or normassociated with the condition. By storing the threshold value inretrievable form, operations to use the threshold values of conditionsin a symbolic AI model may be performed with greater speed and accuracy.In addition, other values associated with vertices may be stored andretrieved based on vertex or norm identifiers, such as transaction costsassociated with transactions between entities.

In some embodiments, the action value may be determined based on futurereward values associated with possible future program state transitionsand may further be modified based on one or more discount factors,transition probabilities, environmental state effects, learning rates,or some function thereof. For example, an action value may be an outputof a policy function. The action value may be affected by various otherprogram state values, intelligent agent parameters, or other values. Thefuture reward values may be based on a set of threshold values encodedin or otherwise associated with the norms simulated to as triggered inthe future program state. In addition, the reward values may be based onadditional factors, such as other program state values, specificdirected graph configurations, types of vertex statuses, or the like. Insome embodiments, determining a set of action values may includegenerating a sequence of program states that include an initial programstate, a set of intermediate program states, and a terminal programstate that indicates that a smart contract program is complete. Forexample, an action value A may be determined using statement 12 belowduring the training of an intelligent agent. As shown below, A(s_(t),e_(t)) is the action value associated with the event e_(t) being causedwhile the program state is in the state s_(t), α is a learning rate,r_(t+1) is the reward value associated with the state change caused byevent e_(t) from the program state s_(t), and γ is a discount factor:

A(s _(t) ,e _(t))←A(s _(t) ,e _(t))+α[r _(t+1) +γA(s _(t+1) ,e_(t+1))−A(s _(t) ,e _(t))]  (12)

In some embodiments, operations to determine an action value for astate-changing event or action may include expanding a directed graph ofa first program state to include its possible child vertices to generatea second directed graph representing a possible future program states.The possible future program states may be reached from a set of possibleevents and may be used to determine a set of reward values associatedwith the set of possible events. The system may determine an actionvalue based on the score changes of that state, a discount factor,transition probabilities to possible future states, and the path scoresassociated with the possible future states. The reward value may bedetermined in various ways. For example, statement 13 below may be usedto determine a path score as a total reward value R_(π)(s) associatedwith a state s, where R_(π)(s′) is a total reward value associated witha possible state s′, s′ and e may represent an index of each possibleprogram state or event caused by an entity, respectively, r is a rewardvalue that may indicate a local, immediate reward corresponding to thetransition from the state s to the state s′, γ is a discount factor, pis a probability value of transitioning from the state s to the states′, and π represents a policy function that provides a probability of anentity causing event e when at the program state is at state s:

$\begin{matrix}{{R_{\pi}(s)} = {\sum\limits_{e}{\pi\left( {e\left. s \right){\sum\limits_{{s\; \prime},r}{p\left( {s^{\prime},{r{\left. {s,e} \right)\left\lbrack {r + {\gamma {R_{\pi}\left( s^{\prime} \right)}}} \right\rbrack}}} \right.}}} \right.}}} & (13)\end{matrix}$

As shown above for statement 13, some embodiments may determine thetotal reward value for a current state based on the expected rewards andcorresponding probabilities of occurrence of future states. For example,the system may traverse through the directed graph of a smart contractprogram and determine a first reward value based on a determination thata first program state corresponds with an event caused by the firstentity satisfying an obligation norm to transfer 300 units to a secondentity as part of a resource allocation agreement. The system may assigna reward value of −300 in association with the first entity and a rewardvalue of +300 in association with the second entity. In someembodiments, these reward value may be used to represent score changesin one of various possible scenarios, such as score changes in anagreement by the first entity to allocate 300 terabytes to the secondentity in return for the second entity reallocating more memory to thefirst entity at a later time. Some embodiments may use the rewards torepresent other score changes, such as changes during transfers ofdigital currency, reputation scores, or the like. If the satisfaction ofthis obligation does not result in the termination of the smart contractprogram but instead in the activation of a first rights norm for thesecond entity to transfer 400 units to the first entity within fiftydays and a second rights norm for the second entity to transfer 310units to the first entity within ten days.

In some embodiments, a time difference may be used in conjunction withthe discount factor to determine a future value. The system may use adiscount factor of 99.5% per day and determine that, based on thisdiscount factor and reward value, the discounted reward value for thefirst possible state value “γ R_(π)(s₁′)” is 311.33 by computing theresult of the expression “0.995⁵⁰×400.” Similarly, the system may use adiscount factor of 99.5% per day and determine that, based on thisdiscount factor and reward value, the discounted reward value for thesecond possible state value “γ R_(π)(s₂′)” is 294.84 by computing theresult of the expression “0.995¹⁰×310.” In some embodiments, the systemmay use of a policy function that determines that second entity has a95% chance of satisfying the first rights norm and a 5% chance ofsatisfying the second rights norm in statement 13 above to determine atotal value for the state R_(π) by computing a total reward value of10.5 by simplifying the expression “−300+95%×311.33+5%×294.84.” Thereward value may be further modified based on vertex categories. Forexample, some embodiments may impose additional reward value deductionsfor failing an obligation, violating prohibitions, or the like.Furthermore, reward values may be further altered based on one or moreentity properties.

In some embodiments, an end state may be used when determining rewardvalues for possible future states. In some embodiments, an end state maybe a terminal program state. A terminal program state for an applicationmay be one in which no additional program states are possible after theapplication has reached the terminal program state. For example, afunction may test whether any norms are triggerable in a program state,and, if the function determines that no norms of the program state aretriggerable, determine that the program state is in a terminal programstate. In some embodiments, the system may determine that a programstate is terminal if a terminal norm is reached, where a norm or itsassociated vertex may be categorized as a terminal norm to indicate thata terminal program state has been reached.

In some embodiments, an end state may be based on an absolute orrelative expiration threshold. For example, some embodiments may includea determination that a program state has reached its expirationthreshold of 30 years, where each program state has an associated timepoint based on a timestamp associated with the satisfaction of the normsthat would result in that program state. For example, a program statemay include an obligation by the first entity to allocate an amount ofcomputing resources to a second entity every month, and determining aset of events that satisfies these obligation norms may includeadvancing the simulated time by one month for each event that satisfiesan obligation norm. If an expiration threshold of the symbolic A modelis one year, the system may determine that an end state has been reachedonce the one-year expiration threshold has been reached. Thus, an eventthat satisfies the twelfth obligation may be associated with a non-zeroreward value that affects the action value of a currently active norm,and an event that satisfies the thirteenth obligation norm may notaffect the action value of the same currently active norm.

In some embodiments, linear algebra direct solution methods may beimpractical for determining reward values due the number of possiblestates. In addition, assigning value in real-world scenarios withincomplete information may provide additional challenges. Suchchallenges may include an inability to fully quantify the risksassociated with certain types of anticipated rewards, the activation ofnorms that allow for new possible actions based on changes in anenvironment, or the like. For example, in response to an electricalblackout that had previously not been occurring, a first-party entityrepresenting a manufacturing center may trigger a rights norm thatrequires a second entity representing a utility company to allocate aspecified amount of electrical energy and terminate the program state,but the satisfaction of such an obligation may be physically impossible.Assigning an action value to a program state may include a determinationof the value based on both the specified amount of electrical energy anda probability value associated with non-delivery of the specified amountof electrical energy.

In some embodiments, action values may be determined by performingrepeated simulations of applying different sequences of events to asmart contract program and updating action values based on intermediateor final results of the repeated simulations. For example, a system maydetermine the action value by using dynamic programming, Monte-Carlosimulations, or temporal-difference learning. In some embodiments, anintelligent agent may perform these repeated simulations using one ormore machine learning models to determine action values associated withthe possible events or actions associated with a program state.Embodiments of these learning operations usable to determine actionvalue(s) and outcome states are described further below for FIG. 15.

In some embodiments, the process 1400 includes determining if there areadditional scenarios for consideration, as indicated by block 1440. Insome embodiments, the system may be configured to consider only acurrent program state. Alternatively, the system may be configured toconsider multiple program states based on the current program state. Forexample, the system may have a pre-arranged set of scenarios whereassets are modified to have greater quantitative resources or fewerquantitative resources. In addition, as discussed further below, thesystem may use scenarios determined based on environmental variablechanges or possible actions from additional entities that are not listedin the entity list of a smart contract program state. For example, thesystem may update action values based determines that there areadditional scenarios for consideration, operations of the process 1400may proceed to block 1408. Otherwise, operations of the process 1400 mayproceed to block 1450.

In some embodiments, the process 1400 includes determining a set ofoutcome program states or a set of outcome scores using the intelligentagent, as indicated by block 1450. The set of outcome program states mayinclude using the operations above to predict one or more possibleoutcome program states. For example, an intelligent agent may use theoperations above to determine a set of possible directed graphsrepresenting in a set of terminal program states. Alternatively, or inaddition, the intelligent agent may predict outcome program states thatare not terminal program states.

The set of outcome scores may be associated with the set of outcomeprogram states and may include various types of values. In someembodiments, the set of outcome scores may include a total score changedetermined from the reward values and the transition probabilities fromthe current program state to a set of terminal program states. In someembodiments, the set of outcome scores may be based on a starting valueassociated with an entity and may also be separated based on vertexcategories or events. For example, a set of outcome scores for an entitymay include a total score owed to the entity based on a first set ofobligation norms, a total score owed by the entity based on a second setof obligation norms, a total score provided to the entity via a firstset of events, and a total score provided by the entity via a second setof events. Alternatively, or in addition, the set of outcome scores mayinclude a probability weight for a cumulative reward score reaching atarget value or for a particular outcome program state or outcomeprogram state value occurring. For example, an outcome score may includea probability weight correlated with the probability that a first entityfails to satisfy an obligation norm, such as by allowing a time point tosatisfy a failure time threshold of the obligation norm.

In some embodiments, the system may determine whether an outcome scoresatisfies a set of outcome score thresholds and, in response, perform anaction in response to the outcome score satisfying the set of outcomescore thresholds. For example, the system may determine that an outcomescore equal to a probability that a set of outcome program states willoccur is greater than an outcome score threshold and, in response,transmit a warning message indicating that the outcome score is greaterthan the outcome score threshold via an API to a device. Alternatively,or in addition, the system may be configured to perform one or moreadditional actions on behalf of one or more entities in response to athreshold be satisfied. For example, in response to an outcome scoresatisfying a net loss threshold, the system may be configured to triggera rights norm that obligates a first entity to return a score value to asecond entity. Furthermore, similar to other methods described in thisdisclosure a plurality of outcome states or outcome scores may be usedto determine a population of outcome states, a population of outcomescores, or their corresponding population scores. A population score mayinclude population statistics such as mean values, median values,kurtosis values, tail risk tolerance, or the like.

FIG. 15 is a flowchart of an example of a process by which a program maytrain or otherwise prepare an intelligent agent to determine outcomeprogram states, in accordance with some embodiments of the presenttechniques. In some embodiments, the process 1500 may includedetermining an expanded directed graph based on a program state, asindicated by block 1504. The program state may include a program statedescribed above for the process 1400. The expanded directed graph may bedetermined by using a directed graph of the program state or a copythereof to determine a first set of possible child vertices that arechild vertices of the active vertices of the directed graph. The systemmay expand the directed graph by associating the vertices of thedirected graph with the first set of possible child vertices. The systemmay then continue expanding the directed graph by determining a secondset of possible child vertices that are child vertices of the first setof possible child vertices and associating the second set of childvertices with their respective vertices in the first set of possiblechild vertices. The system may continue simulating evolving programstate by expanding the directed graph to until each terminal vertex isreached, or until some other end state is reached, where the expandeddirected graph may be associated with a possible outcome program stateafter the simulated state evolution. Similar to those described above, aterminal vertex of a directed graph of a smart contract programimplemented with a symbolic AI model may be vertex that has no childvertices, may be described as leaf of a directed graph, and may beassociated with a terminal state.

Alternatively, instead of expanding a directed graph of an applicationstate directly, the system may simulate events that trigger the verticesof the directed graph. As described above, these simulated events mayinclude simulations of one or more entities that cause events based onthe norms of the directed graph. For example, the system may simulatethe triggering of a first obligation norm associated with a conditionrequiring that a first entity release at least 500 GB of memory inresponse to a simulated event of the first entity releasing 850 GB ofmemory.

In some embodiments, the system may traverse the directed graph todetermine a set of score-based reward values, as indicated by block1508. In some embodiments, the system may traverse the directed graphwhile expanding the directed graph. For example, as the system expands adirected graph, the system may also record the score changes associatedwith outcomes of satisfying or failing the norms of the directed graph.An intelligent agent may use these score changes to determinescore-based reward values associated with transitions between differentprogram states. For example, failing a vertex categorized as anobligation norm may set a vertex status to indicate that the obligationnorm is failed and activate a child vertex associated with a normcondition having a threshold value equal to “−10.” In response, thescore-based reward value associated with the state change may be adecrease of 10.

In some embodiments, the system may traverse the directed graph afterperforming one or more expansion operation on the directed graph inorder to determine score-based reward values based on the outcomes ofsatisfying or failing each norm of the directed graph. As describedabove, an obligation norm may be failed if it is not satisfied if a timepoint reaches a time threshold of the obligation norm. Furthermore,during simulation of events, an event may include an event that wouldsatisfy the conditions of a norm that occurs after a failure timethreshold, and the intelligent agent may determine that a vertex shouldbe associated with a status indicating failure if response to adetermination that the time point associated with the condition isassociated with a failure time threshold.

In some embodiments, the system may update a set of data structureinstances (e.g., an array, a vector, a list, or the like) to store theset of reward values associated with each program state or program statetransition. In addition, the set of data structures may include labelsfor the program states of an application, the transitions between eachof the program states, action values associated with each transition, orthe like.

In some embodiments, the process 1500 may include determining a set oftraining values for an intelligent agent, as described in block 1512.The set of training values may include training inputs and trainingobjectives, where the training inputs and training objectives may beobtained using one or more various methods. An intelligent agent mayinclude one or more machine learning models. Example machine learningmodels may include supervised learning models, unsupervised learningmodels, semi-supervised learning models, reinforcement learning models,or the like. In some embodiments, using the machine learning model mayinclude training or using a set of neural networks, such as one or moreof the neural networks described above. Various types of neural networktraining methods may be utilized to determine the parameters of a neuralnetwork. In some embodiments, the neural network training method mayinclude a backpropagation operation that includes passing a traininginput through the neural network to determine a neural networkprediction. The neural network prediction may be compared to a trainingobjective to determine a loss function. The loss function may then bepropagated back to update the parameters of the neural network using agradient descent method. This process may be repeated until the neuralnetwork prediction sufficiently matches the training objective to updatethe parameters of the neural network.

In some embodiments, the training inputs and training objectives may beacquired from histories of similar smart contract programs.Alternatively, or in addition, the training inputs and trainingobjectives may be acquired from simulations of possible future events.Furthermore, the training inputs and training objectives may depend inpart on the set of learning systems used to determine outcome programstates or outcome scores. In some embodiments, training inputs may beiteratively generated during each iteration of training. For example,the intelligent agent may generate a first set of training inputs duringa training iteration, where a training iteration may include simulatingevolving program state using a pseudorandom method to determine whichset of vertices to include in a path through a directed graph. Thesimulated state evolution may also include updating a set of actionvalues or other parameters based on the results of the simulated stateevolution and generating a second set of training inputs based on theupdated parameters. The training inputs may be generated during asimulation of the satisfaction of norms or failure to satisfy norms. Insome embodiments, the determination of the training inputs may be basedon one or more entity properties. For example, an entity may include aset of values defining a probability distribution, where the probabilitydistribution may be used to determine whether the entity will satisfy orfail an obligation, and the system may sample from the probabilitydistribution to determine one or more values for a training input.

In some embodiments, the process 1500 may include determining a set ofaction values or other set of parameters of the intelligent agent basedon the set of reward values and training values, as indicated by block1516. An action value may represent a weight assigned to a possibleaction of an entity that causes a state change in the program state ofan application. In some embodiments, the action value may be determinedbased on a set of paths through a directed graph, where each respectivepath of the set of paths includes a respective plurality of vertices andrespective directed edges connecting each vertex of the respectiveplurality of vertices with at least one other vertex. In someembodiments, a set of action values may be determined based ondetermining the sequence of reward values associated with the respectiveset of vertices for each respective path of a set of paths. The actionvalue may be further determined based on training values obtained fromhistorical data or acquired from simulations.

In some embodiments, the intelligent agent may include a model-basedreinforcement learning model as a part of a machine learning model.Using a model-based reinforcement learning model may include using aMarkov decision process comprising a state transition model to simulateprogram state evolution by predicting a next program state and a rewardmodel to determine an expected reward during the transition to the nextprogram state. In some embodiments, the system may continue simulatingthe evolution of a program state through a sequence of states bytraversing through a directed path based on the vertices and edges of adirected graph until a terminal program state is reached. The evolutionof the program state may be simulated over a set of simulated stateevolutions, where each simulated state evolution in the set of simulatedstate evolutions may be associated with a path through the directedgraph. The associated paths may be used to form a set of paths. As asystem traverses a directed path, the system may determine a subset ofreward values associated with the directed path and may use this subsetof reward values to determine a path score or otherwise use the subsetof reward values to determine an action score. In some embodiments, theexpected reward value between each state change may be based on the setof score-based reward values determined above, where each path in a setof paths may be associated with a different expected total reward value.In addition to score changes associated with vertices, the system mayalso modify action values based on the satisfaction or failure ofobligation norms. For example, the system may decrease the reward valueby a failure penalty associated with the transition from a first stateto a second state if the transition was the result of failing anobligation norm. In some embodiments, the failure penalty may be apre-determined value.

Various reinforcement learning models may be implemented to determinethe action values using a neural network of the intelligent agent, suchas a policy gradient approach, a Q-learning approach, a value-basedlearning approach, some combination thereof, or the like. In someembodiments, the system may use a policy gradient approach. For example,using a policy gradient approach may include using a set of policyweights equal to or otherwise based on the action values, wherein eachpolicy weight corresponds with a possible event that an entity may causeat one or more program states represented by a configuration of adirected graph. In some embodiments, the intelligent agent may performrepeated iterations of the simulated state evolution using a set oftraining inputs to determine the results of an entity acting accordingto instructions based on the policy function, wherein each intermediateoutcome or terminal outcome may be used to increase or decrease acorresponding policy weight. The set of simulated state evolutions maybeing performed continue until a training objective is achieved, wherethe training objective may include a maximization of a total rewardvalue, a minimization of a loss function, a minimization of the numberof obligation norms having a status as being failed, or the like.Various policies may be used, such as softmax policy, Gaussian policy,or the like. In some embodiments, the number of iterations may begreater than 10 iterations, greater than 1000, greater than 10,000iterations, greater than 100,000 iterations, greater than 1,000,000iterations, or the like.

In some embodiments, the system may use a Monte Carlo Tree Search (MCTS)operation to determine the set of action values by sequentiallyselecting vertices of a directed graph based on the graph edges. Thesystem may use a trained neural network to determine an initial set ofactions causable by a first entity and their associated initial set ofaction values. Each action of the initial set of actions may beassociated with an initial action value also determined by the trainedneural network. The initial action value may be based on the score-basedreward values determined above and may be correlated with a probabilityof the first entity performing that associated action. In someembodiments, the initial action value may be the output of a softmaxfunction. For example, the initial set of actions determined by a neuralnetwork may include a first action associated with a pre-processedweight equal to 3, a second action associated with a pre-processedweight equal to 1, and a third action associated with a pre-processedweight equal to 2. The intelligent agent may apply a softmax function tothe pre-processed weights to determine an initial set of action valuesapproximately equal to [0.665, 0.090, 0.245].

In some embodiments, a system may perform a comprehensive set of treesearch operations through each vertex of a directed graph until everyend state is achieved. For example, if there are five paths from astarting vertex to a set of end states, some embodiments maycomprehensively traverse each of the five paths to determine five setsof reward values associated with each of the five paths. In someembodiments, the system may determine each possible path from a startingvertex to each terminal vertex and then determining the associatedreward values and probabilities associated with the vertices of eachrespective path to determine action values associated with entityactions. Alternatively, the system may determine multiple sequences ofevents caused by one or more entities to determine a set of pathsthrough a directed graph such that at least one of the sequences ofevents is associated with each of the paths and every possible path fromthe starting vertices to a terminal vertex or another vertex associatedwith an end state is one of the set of paths.

In some embodiments, a directed graph may be searched usingprobabilistic methods. For example, if there are five paths from astarting vertex to a set of end states, some embodiments may traversefour of the five paths and avoid simulating a selection of at least oneof the vertices of the directed graph. Various implementations of aprobabilistic method may be used. For example, action values of adirected graph may indicate that an obligation norm vertex may a 99.99%chance of being satisfied and a 0.01% chance of being failed forsimulation. Over the course of 100 searches through a directed graph tosimulate evolving program state from an initial program state thatstarts at the obligation norm vertex, the system may use a random,pseudorandom, or quasi-random number generation method to determinewhether the obligation norm vertex is satisfied or failed. Each searchthrough a directed graph may include determining a set of computedvalues using the random, pseudorandom, or quasi-random number andselecting the next vertex based on the set of computed values. In someembodiments, each search may continue until a terminal vertex or someother vertex associated with an end state is reached. The search may berepeated for multiple iterations. For example, a search may be repeatedfor a directed graph with new computed pseudorandom values to determinea corresponding new path through the directed graph more than 10 times,more than 1000 times, more than 10,000 times, more than 100,000 times,more than 1,000,000 times, or the like.

In some embodiments, it is possible that the use of a random,pseudorandom, or quasi-random number generation method to select pathsbased on these action values may skip one or more vertices such that nopaths that include activated vertices resulting from failing theobligation norm. Various random, pseudorandom, or quasi-random numbergeneration methods may be used, such as a method based on a physicalphenomenon or a computational method based on a seed value. Examplerandom, pseudorandom, or quasi-random number generation methods mayinclude wideband photonic entropy source-dependent methods, linearcongruential generator methods, middle square methods, middle squareWeyl sequence methods, permuted congruential generator methods,low-discrepancy sequence methods, or the like.

In some embodiments, the neural network may include a deep neuralnetwork usable as a fuzzy story of values. For example, the deep neuralnetwork may be a deep convolutional neural network, deep graph neuralnetwork, deep recurrent neural network, some combination thereof, or thelike. The neural network may receive the directed graph vertices andedges as input and be trained to predict a return value based on thereward values determined above. In some embodiments, the trainingobjective may be based on a total reward value. Alternatively, or inaddition, the training objective may be based on the satisfaction ofeach obligation norm or non-satisfaction of a prohibition norm. Forexample, a training objective may be classified as positive or negative,where a positive outcome is associated with a terminal program state inwhich all obligation norms are satisfied, and a negative outcome isassociated with all other possible terminal program states.Alternatively, or in addition, a training objective may be based on bothreward values and vertex categories. For example, a positive outcome maybe associated with all terminal program states in which all obligationnorms are satisfied or over 100% of a target score threshold issatisfied, where the target score threshold is based on score transfersdetermined from the reward values.

After performing each of a set of searches in a set of MCTS operations,the intelligent agent may determine a path score associated with thepath taken through the directed graph based on the reward valuesassociated with the vertices of a directed path. A directed path mayinclude a linkage of graph vertices via their connected directed edges,where the path direction may be determined by the edge directions of thedirected edges. For example, if performing an iteration of the MCTSoperation resulted in a first path that started a first vertexassociated with a first reward value equal to “−400,” a second rewardvalue of “−100,” and a third reward value of “+1100,” the system maydetermine that a total reward value is equal to “+600” by summing thethree individual reward values. As further described above, the systemmay also include vertex category based modifications to the rewardvalue. For example, if the system determines that the path includes anorm vertex having a status indicating failure, the system may reducethe total reward value from “+600” to “−9000” based on a failure penaltyequal to “−9600.” The intelligent agent may then update the aggregatescore associated with the action based on the total reward value. Aftera loop through an MCTS operation, the system may also retrain the neuralnetwork based on the aggregate score or total reward values to determinethe values for a next run-through of the MCTS operation.

In some embodiments, a deep Q-learning approach may be taken toimplement a reinforcement learning method. For example, an intelligentagent using a deep Q-learning approach may use a multi-layer neuralnetwork to determine internal reward values based on a program state.The intelligent agent may include one or more various types of neuralnetworks, such as a convolutional neural network or a graph neuralnetwork. Graph neural networks may include graph auto-encoders, graphconvolutional neural networks, graph generative networks, graphattention networks, and graph spatial-temporal networks. In someembodiments, a graph neural network may be useful to receive thestructure of a directed graph representing a smart contract programstate as an input to generate one or more output values. Duringtraining, the intelligent agent may also store previous program states,program state transitions (or the action used to cause the transition),reward values, and outcome program states as experiences in a memorybuffer and select these experiences from the memory buffer forretraining the neural network.

In some embodiments, the intelligent agent may implement acounterfactual regret minimization method when determining an actionvalue. Use of a counterfactual regret minimization method may includeoperations described in “Deep Counterfactual Regret Minimization”(Brown, Noam; Lerer, Adam; Gross, Sam; Sandholm, Tuomas. Nov. 1, 2018,arXiv: 1811.00164), which is hereby incorporated by reference. Toimplement a counterfactual regret minimization operation, the parametersof the intelligent agent may include one or more strategy profile valuesand a set of counterfactual regret values, wherein each counterfactualregret value of the set of counterfactual regret values is associatedwith an action. In some embodiments, the counterfactual regret value mayreduce a corresponding action value and may represent the regret of notfollowing a strategy profile. For example, a counterfactual regret valuefor an action may be quantitatively defined as a difference between atotal expected loss of an algorithm using a combination of strategiesand a minimum total loss when following a single strategy.

In some embodiments, a counterfactual regret may be determined based ona calculation of counterfactual utility values for each of a set ofpossible events that an entity may cause, where a set of strategy valuesmay be used to assign probabilities to each event of the set of possibleevents. The strategy values may be determined using a strategy function,where the strategy function for a given program state may be based on aprobability distribution function over a set possible events causable byan entity when the program is at the given program state.

Determining a counterfactual utility value of an event caused by anentity may be based on the set of reward values determined above and aset of probabilities. A probability of the set of probabilities mayindicate reaching a terminal program state from the current programstate after the entity causing for the entity, where the computation mayinclude different paths from a set of paths, where each path of the setof paths may start at an initial vertex and end at a terminal vertex. Insome embodiments, determining a counterfactual utility value of anaction for a first entity may include a sum of products, where each ofthe products is a product of a total reward value associated with aterminal program state and a corresponding probability of reaching therespective terminal program state from the current program state. Insome embodiments, an initial program state may be changed to one or moreintermediate program states before being changed to a terminal programstate. For example, a counterfactual regret value u_(i) for entity i maybe determined using statement 14 below, where π^(σ)(se,s′) representsthe probability of reaching a terminal program state s′ starting at aprogram state se, where se may represent the subsequent program stateafter a state-changing event e is received, W is the set of terminalprogram states, π_(−i) ^(σ)(s) represents a counterfactual reachprobability of reaching the state s assuming the strategy σ is used, andu(s′) represents a path score as determined by incrementing/decrementingthe cumulative reward value when changing from the current state s tothe final state s′ based on the reward values determined above:

$\begin{matrix}{u_{i} = {\sum\limits_{{s \in I},{h^{\prime} \in W}}{{\pi_{- i}^{\sigma}(s)}{\pi^{\sigma}\left( {{se},s^{\prime}} \right)}{u\left( s^{\prime} \right)}}}} & (14)\end{matrix}$

In some embodiments, the system may determine the probability ofreaching a respective terminal program state using a set of strategyvalues, where the strategy values may be based on an information setrepresenting the amount of information about the smart contract programstate available to the first entity. In some embodiments, each entitymay have all information available about the smart contract programstate, resulting in a perfect information set. Alternatively, one ormore of the entities may have incomplete information about the smartcontract program state.

In some embodiments, the intelligent agent may execute a regret matchingroutine to calculate a counterfactual regret value for one or morepossible actions. Using the regret matching routine may includedetermining a reward value for an entity associated with the entitycausing an event at a time point, where the reward value may be equal toor otherwise based on the score changes caused by the event. Using theregret matching routine may also include determining a total rewardassociated with causing the event at the time point. Using the regretmatching routine may also include determining an additional reward valueassociated with causing the event based on a set of strategy values.Using the regret matching routine may also include determining anadditional reward value associated with causing the event based on a sumof total reward values associated with using multiple sets of strategyvalues. Using the regret matching routine may also include updating aset of strategy values at the time point based on a regret value for anaction and a total regret value across all actions. For example, theregret matching routine may include updating a strategy value based on aratio of a regret value acquired of performing a first action with afirst information set and a sum of regret values, where each regretvalue of the sum regret values is based on a different action and thefirst information set.

In some embodiments, additional strategic behavior may be considered byusing neural networks to consider the viability of additional eventscausable by an entity that would be ignored or otherwise grouped in acategory. For example, some embodiments may perform one or moreoperations similar to those used in the MuZero algorithm, where theMuZero algorithm is described in “Mastering Chess and Shogi by Self-Playwith a General Reinforcement Learning Algorithm” (Schrittwieser,Antonoglou, Hubert, Simonyan, Sifre, Schmitt, Guez, Lockhart, Hassabis,Graepel, Lillicrap, Silver. Submitted 19 Nov. 2019. arXiv: 1911.08265),which is hereby incorporated by reference. Using a MuZero algorithm mayinclude performing a set of self-play operations to determine viableevent-causing actions and a set of a training operations based on theresults of the self-play operations to determine score-maximizingbehavior.

In some embodiments, the intelligent agent may store multiple versionsof the neural network in a neural network parameter store and outcomeprogram states in an outcome program state store. In some embodiments,the outcome program states may include results from a set of self-playoperations or actual outcome program states from previous executions ofa smart contract program. During a self-play operation, the intelligentagent may obtain an initial set of events causable by an entity, such astransferring a set of different score amounts at a set of different timepoints, changing one or more environmental states, or the like. In someembodiments, the intelligent agent may determine an initial set ofevents causable by an entity based on the conditions of the smartcontract program. For example, if a condition of an obligation norm ofthe smart contract program includes a score transfer threshold requiringthe allocation of at least 10 gigabits per second (GB/s) from an entitywith respect to a data pipeline, the intelligent agent may vary thescore transfer threshold to a set of modified values including 50% ofthe score transfer threshold and 200% of the score transfer threshold,resulting in the set of modified values of 5 GB/s and 20 GB/s. Theintelligent agent may then include the first entity allocating 5 GB/s,allocating 10 GB/s, and allocating 20 GB/s as three possible events inthe initial set of events.

The intelligent agent may use the initial set of events to simulate oneor more entities that cause events using an initial set of self-playoperations. A self-play operation may include using an initial set ofrandom decisions representing actions or events caused by each entity,such as via a Monte Carlo Search Tree operation (MCTS) described furtherbelow, until a terminal program state is reached. The intelligent agentmay then store the terminal program state, an associated terminal stateoutcome score, or other outcome program states or their associatedoutcome scores in the outcome program state store. The results of theinitial set of self-play operations may be used to train a plurality ofneural networks. The intelligent agent may perform multiple iterationsof the self-play operations sequentially or in parallel for apre-determined number of times, such as more than 10 times, more than100 times, more than 10000 times, more than 10⁶ times, or the like.

In some embodiments, the intelligent agent may use the results of theinitial set of self-play operations to train a plurality of neuralnetworks in combination with additional MCTS operation. The intelligentagent may begin at a first vertex of a directed graph and traverse thedirected graph by proceeding to an adjacent vertex determined by the setof directed edges connected to the first vertex. Each configuration ofthe directed graph may be of a different program state or be otherwiseassociated with a different program state. For example, the intelligentagent may traverse to a first child vertex of an initial directed graphfrom a starting vertex. The intelligent agent may proceed to expand thedirected graph to simulate evolution of its associated program stateuntil arriving at a terminal child vertex and ending at a terminalprogram state. In some embodiments, performing an MCTS operation mayinclude determining a maximum upper confidence bound (UCB) score, wherethe UCB score may be based on an exploration weight and a total weightscore, where the exploration weight is correlated with exploringless-visited nodes and the total weight score is correlated withfollowing the vertices associated with the greatest total reward values.

The MCTS operation may include keeping track of the number times avertex is visited, which entity that is responsible each change instate, a prior probability value correlated with the probability of thevertex being visited in an iteration of a simulated state evolution, abackfilled vertex sum value, a hidden state value, and a predicted totalreward value for an entity, or the like. After each state change, theintelligent agent may use a first neural network to determine aninternal predicted value and hidden state value associated with thevertex. The intelligent agent may also use a second neural network todetermine a policy weight based on the hidden state value. Theintelligent agent may then expand the directed graph again, if possible,and update the policy prior value with a new policy weight using thethird neural network. The internal predicted value and hidden statevalues of the vertices of the expanded directed graph may then bebackpropagated up the directed graph to a root vertex of the directedgraph.

In some embodiments, the intelligent agent may initialize each neuralnetwork of the plurality of neural networks and set a learning ratebased on the number of previous training iterations. As described above,the first neural network may be trained to generate a set of internalpredicted values and hidden state values, forming an internalrepresentation of a program state. The second neural network may then betrained to formulate a set of policy weights for a policy function basedon the internal representation. A third neural network may then betrained to predict a network-predicted reward value based on the policyweights and internal predicted value, where the action value may beequal to or otherwise based on the network-predicted reward value.

In some embodiments, the system may determine a set of possiblestate-changing events from a first vertex using one of the neuralnetworks described above, where more than one event may cause a sameadjacent vertex to be selected on the directed graph. In someembodiments, the intelligent agent may generate a decision treeassociated with but not identical to the directed graph. For example,the intelligent agent may generate a decision tree having one or moredecision tree vertices for each smart contract directed graph vertex,where each respective decision tree vertex associated with a respectivesmart contract directed graph vertex is associated with a differentevent or action that would trigger the respective smart contractdirected graph vertex.

In some embodiments, the efficiency of tree traversal during a set ofMCTS operations may be increased by limiting the outcomes based on theset of vertex categories assigned to each vertex. For example, a systemmay encounter a vertex associated with a category value indicating thatthe vertex represents or is otherwise associated with an obligationnorm. In some embodiments, an obligation norm may be categorized basedon a set of types of statuses, set of behaviors, or set of properties,where a status change to a satisfied status may result in the activationof a first set of child vertices and a status change to a failed statusmay result in the activation of a second set of child vertices. Forexample, a set of conditions encoded in an obligation norm associatedwith a vertex may be satisfied, triggering the vertex by changing thestatus of the vertex (“vertex status”) to a satisfied status andactivating a first set of child vertices connected to the vertex via afirst set of graph edges. In addition, if the obligation norm associatedwith a vertex is not satisfied before a failure threshold is satisfied,the system may trigger the vertex by changing the vertex status to afailed status and activate a second set of child vertices connected tothe vertex via a second set of graph edges. In some embodiments, one ormore obligation norms may be set to an unsatisfied status, where anunsatisfied status may indicate that the one or more obligation normshave not been satisfied but are still satisfiable.

In some embodiments, separate sets of events may be determined based onentities. For example, a first entity may be set as a first-actingentity and a second entity may be set as a counterparty entity, whereeach of the first-acting entity and counterparty entity may be able tocause different types of events. For example, a first-acting entity maycause events that satisfy a set of first-acting entity conditions thatincludes allocating different amounts of resources at different times,recalling allocated resources, or modifying a property of an allocatedresource. Similarly, the resource-using entity may be able to cause oneor more of a second set of events to trigger a second set of conditionsassociated with a second set of vertices triggerable by events causableby the counterparty entity (“set of counterparty conditions”). Forexample, the set of counterparty conditions may include verifying thatan allocated resource satisfies a set of resource properties, confirmingresource delivery, providing a digital asset after confirming theallocation of a resource, or the like.

As described above, when implementing a set of MCTS operations, thesystem may determine a next possible state s+1 for each state s based ona set of heuristic values associated with the set of events causable byan entity. A heuristic value may be determined using a function based ona ratio of an aggregate score associated with an event caused by anentity, the number of times that the event has been performed whileperforming the set of MCTS operations, a total number of iterations, andan exploration parameter. For example, the heuristic value may bedetermined as the sum of a first value and a second value. The firstvalue may be a ratio of the aggregate score value to the number of timesthat an action has been taken while performing the set of MCTSoperations. The second value may be a product of the explorationconstant and a ratio of a logarithm of the total number of iterationsperformed while performing the set of MCTS operations and the totalnumber of iterations. In some embodiments, each heuristic value may beassociated with an event causable by an entity and may be used todetermine which event is most probable. For example, if a firstheuristic value is greater than a second heuristic value, an eventassociated with the first heuristic value may have a greater probabilityof being selected than an event associated with the second heuristicvalue.

Alternatively, or in addition, the heuristic value may be based on thevertex categories assigned to each respective vertex activated by therespective action. For example, if a first event is associated with afailure to satisfy an obligation norm, the system may automaticallydeduct a pre-determined failure penalty from an associated firstheuristic value or set the first heuristic value to a pre-determinedvalue. Similarly, if a second event satisfies a rights norm, the systemmay automatically increase the associated second heuristic value by apre-determined amount from or set the second heuristic value to apre-determined value. By using the vertex categories implemented in asymbolic AI model, the system may increase the efficiency and accuracyof operations to determine outcome program states based on a directedgraph of an initial program state.

In some embodiments, the process 1500 may include determining a set ofoutcome program states or set of outcome scores or other parameters ofthe intelligent agent based on the set of action values or other set ofparameters of the intelligent agent, as indicated by block 1528. In someembodiments, the intelligent agent may determine an outcome programstate by providing a probability distribution associated with one ormore possible outcome program states. For example, the intelligent agentmay use a trained neural network described above to determine that thereis a 25% chance that a smart contract program will end at a firstterminal program state, a 35% chance that a smart contract program willend at a second terminal program state, and a 40% chance that a smartcontract program will end at a third terminal program state. In someembodiments, an outcome program state may be associated with an outcomescore, where the outcome score may be based on a total reward value, atotal reward range, a risk of failure value, or the like. In someembodiments, a smart contract program may receive an event associatedwith an action performed by a counter entity that causes a change to aprogram state into a subsequent actual outcome program state that is notin the set of predicted outcome program states.

In some embodiments, a set of expected outcome program states determinedusing the operations above may be used as part of an unexpected eventthreshold. The system may determine that an unexpected event thresholdis satisfied if an actual outcome program state is not in the set ofexpected outcome program states. In response, the system may perform anaction such as transmitting a message indicating that an unexpectedevent had occurred. For example, an intelligent agent may determine thatthe set of expected outcome program states includes a first programstate and second program state. The first example program state may beassociated with a counterparty entity satisfying an obligation norm bytransferring a digital asset required to access a restricted database,and the second example program state may be associated with thecounterparty entity satisfying a right to rescind the request foraccess. If the counterparty entity instead satisfies a rights norm tooverride the security request, which results in a third example programstate that is not in the set of expected outcome program states, thesystem may determine that an unexpected event threshold is satisfied. Inresponse, the system may transmit a message indicating that anunexpected event threshold was satisfied. Furthermore, in someembodiments, the system may determine a behavior pattern of an entitybased on the events caused by the entity. In some embodiments, thebehavior pattern may be analyzed using a neural network to determineanomalous behavior indicating that the entity is likely to fail a futureobligation or cancel the smart contract based on past behavior of thesame entity or similar entities.

FIG. 16 is a flowchart of an example of a process by which a program maydetermine an inter-entity score quantifying a relationship between apair of entities across multiple smart contract programs, in accordancewith some embodiments of the present techniques. In some embodiments,the process 1600 may include determining a set of vertices of atransaction graph based on a set of smart contract programs, asindicated by block 1604. In some embodiments, a transaction graph may beused to track score exchanges, asset transfers, or other transactionsbetween multiple entities across a set of smart contract programs. Forexample, the vertices of the transaction graph (“transaction graphvertices”) may represent entities of the set of smart contract programs,and a set transaction graph edges may represent score changes, where thedirections of the set of transaction graph edges may be used torepresent a net transaction score change. The entity of a transactiongraph may refer to a vertex of the transaction graph (“transaction graphvertex”) that is associated with the entity.

The set of smart contract programs may be stored on a single set ofcomputing devices. Alternatively, one or more of the set of smartcontract programs may be stored on different computing devices. Thesystem may determine the vertices of a transaction graph based on one ormore entities stored in an entity list of a smart contract program. Forexample, a system may access a set of five smart contract programs thatinclude a total of seventy-five entities and generate a transactiongraph having seventy-five vertices, where each respective transactiongraph vertex of the transaction graph is associated with one of theseventy-five entities. In some embodiments, each vertex of thetransaction graph may also have an associated smart contract list, wherethe associated smart contract list includes identifiers for each smartcontract program that lists the entity. For example, a first vertex mayhave an associated smart contract list comprising a first smart contractprogram identifier and a second smart contract program identifier.

In some embodiments, the process 1600 may include determining a set ofthe transaction graph edges based on the set of smart contract programs,as indicated by block 1612. The set of transaction graph edges mayrepresent categorical or quantitative relationships between the entitiesrepresented by the transaction graph vertices. In some embodiments, thesystem may expand the directed graphs of the set of smart contractprograms and determine their associated outcome program states todetermine possible transactions between the different entities of thetransaction graph. For example, the system may expand a smart contractdirected graph and review each norm vertex of the smart contractdirected graph to detect a first score change between a first entity anda second entity, a second score change between the first entity and athird entity, and a third score change between the second entity and thethird entity. The system may update a first transaction graph edge of atransaction graph between the first entity and the second entity basedon the first score change, a second transaction graph edge between thefirst entity and the third entity based on the second score change, anda third transaction graph edge between the second entity and the thirdentity based on the third score change.

In some embodiments, the system may traverse a set of directed graphs ofa set of smart contract programs to modify the value associated with atransaction graph edge based on transactions between entities indicatedby the directed graph vertices. For example, the system may traverse aset of directed graphs of program states, where the set of directedgraphs includes a first smart contract program directed graph and asecond smart contract program directed graph. If the first smartcontract program directed graph includes a score change based on acondition requiring that a first entity allocates 30 terabytes of memoryto a second entity, and the second smart contract program directed graphincludes a score change based on a condition requiring that the secondentity allocate 35 terabytes of memory to the first entity, thetransaction graph edge may be updated after a system traverses throughboth smart contract program directed graphs. The update may change a setof score changes associated with the transaction graph edge to indicatea transfer of five terabytes from the second entity to the first entity.

Furthermore, in some embodiments, each respective smart contract programmay be associated with a respective contribution weight for each entityin the transaction graph, where the respective contribution weight mayindicate the proportional weight that the smart contract program has ona score change associated with the entity. For example, if a first smartcontract program increases the score change for an entity by 5 and asecond smart contract program increases the score change for the entityby 10, the first smart contract program may be associated with a firstcontribution weight of 33.3% for the entity and the second smartcontract may be associated with a second contribution weight of 66.7%for the entity. In some embodiments, the set of contribution weights maybe used to determine which smart contracts have had a greatest pastimpact on a set of entities or which are anticipated to have thegreatest future impact on the set of entities.

In some embodiments, the process 1600 may include obtaining a firstentity and second entity for transaction scoring, as indicated by block1628. In some embodiments, the first entity and second entity may beexplicitly provided by a user as inputs or selected by default. Forexample, a user signed in as the first entity or otherwise representingthe first entity may use a graphic user interface and select a secondentity. Alternatively, or in addition, the system may be instructed togenerate a list of transaction scores and, in response, systematicallyperform one or more operations described in this disclosure for each ofa selected set of pairs of entities, where the first and second entitiesare one of the pair of entities in the selected set of pairs ofentities.

In some embodiments, the process 1600 may include determining a set oftransaction paths between a first entity and a second entity based onthe transaction graph, as indicated by block 1636. A transaction pathmay include a directed sequence of transaction graph edges starting at afirst vertex and ending at another vertex, where each transaction graphedge may represent a transaction. For example, if a first transactiongraph edge is directed from a first entity to a second entity, a secondtransaction graph edge is directed from the first entity to a thirdentity, and a third transaction graph edge is directed from the secondentity to the third entity, a first path from the first entity to thethird entity may include the first transaction graph edge, and a secondpath from the first entity to the third entity may include the secondtransaction graph edge and the third transaction graph edge.

In some embodiments, the path may be determined using one or more pathsearch criteria. For example, a path search criterion may include acriterion that the path between a first entity and a second entityincludes an intermediate entity, wherein each transaction graph edge ofthe path represents an obligation to transmit an amount of assets. Insome embodiments, the path search criteria may include one or morecriteria to filter out transaction paths that include transaction graphedges that are below a certain value. For example, the set of pathsearch criteria may include a criterion that each transaction of atransaction path involves a transaction score greater than a transactionpath threshold, where the transaction path threshold may be set to anyvalue. In some embodiments, the path search criteria may be used todetermine system vulnerabilities. For example, a transaction graph edgerepresenting a token exchange between a first entity and a second entitymay be mediated by a verification entity, where the first entity andsecond entity are both blind to each other's identities, but a failureto satisfy an obligation norm on the part of the second entity mayprevent the first entity from securing an asset from the third entity.The system may determine a transaction path that includes a set oftransaction graph edges to determine an exposure to failure experiencedby the first entity with respect to an action or inaction of the secondentity.

In some embodiments, the process 1600 may include determining aninter-entity score between a first entity and a second entity based onthe set of transaction paths, as indicated by block 1642. Theinter-entity score may represent one of various types of metrics and maybe used to determine an entity reputation score. In some embodiments,the inter-entity score may indicate a vulnerability score, wherepreviously-undetected vulnerabilities based on relationships between afirst entity and a second entity are detected by including a set oftransaction paths that include additional entities as intermediatetransaction graph vertices of a transaction graph. In some embodiments,the vulnerability score may also be based on observable states of anentity. For example, a first, second, and third entity may represent aweb application, a hosting application relied upon by the webapplication, and a cloud-connected server supplying memory resources tothe hosting application, respectively. The system may assign avulnerability score to quantify the vulnerability of the first entity toa failure of the second entity based on a net amount of computer memorythe third entity is to allocate to the hosting application via anobligation norm of the cloud-connected server. The system may thendetermine an inter-entity score between the first entity and the thirdentity based on this vulnerability score.

In some embodiments, the inter-entity score may be used to update anaction value. For example, some embodiments may determine an actionvalue based on a set of reward values that includes a first rewardvalue, where the first reward value is based on a condition requiringthat a first entity transfer a score amount to a second entity. Thefirst entity may have an inter-entity score with respect to a thirdentity, where the inter-entity score exceeds an inter-entity scorethreshold. In response to the inter-entity score exceeds an inter-entityscore threshold, the first entity may modify the first reward value,such as by reducing the reward value. Alternatively, or in addition, thesystem may modify the first reward value based on a detected change inthe status of the third entity. For example, if it is determined thatthe third entity is in a state that indicates it is unable to satisfy aset of obligation norms with respect to the first entity, the system mayreduce the first reward value. In some embodiments, changing the rewardvalue may be directly based on an entity reputation score, and theentity reputation score may be based on one or more inter-entity scores.

In some embodiments, the inter-entity score may indicate the detectionof a cyclical path that begins and ends at a same entity. For example,the system may determine that a transaction path is cyclical, where afirst entity is obligated to transfer a first amount of assets to asecond entity, the second entity is obligated to transfer the firstamount of assets to the third entity, and the third entity s obligatedto transfer the first amount of assets back to first entity. A systemmay determine a cyclical path based on this relationship by detectingthat the transaction path representing this relationship may have a samevertex as the start and end of the transaction path with a same orsimilar amount for each transaction graph edge of the transaction path.An inter-entity score may be determined based on this path based on anaverage amount of assets to be transferred along the cycle and includeor otherwise be associated with an identifier indicating that theinter-entity score reflects a cyclical path.

In some embodiments, the system may determine that an entity isassociated with a transaction that includes an obligation norm having astatus indicating failure and, in response, reduces a reward associatedwith other possible transactions involving the entity. For example, atransaction graph may include a first entity that is scheduled totransfer a score value to a second entity at a future time, where thescore value being transferred may be used to determine a reward valuesuch as the reward values described above. The system may detect thatthe first entity had failed a previous obligation to transfer adifferent score value to a different entity and, in response, reduce thereward value based on historical data indicating that the first entityhas a reduced probability of satisfying its obligation to otherentities.

FIG. 17 depicts a set of directed graphs representing possible outcomeprogram states after a triggering condition of a prohibition norm issatisfied, in accordance with some embodiments of the presenttechniques. The set of directed graphs 1700 includes a set of obligationnorms representing affirmative covenants and prohibition normsrepresenting negative covenants. By using obligation norms andprohibition norms as encoded using the methods described in thisapplication, these covenants may be programmatically enforced andanalyzed without the writing or using ad-hoc computer code based on oneor more detected events. In some embodiments, an event may be directlysensed by the computer system and rendered comparable to one or morenorm conditions. Alternatively, or in addition, an external party, suchas a network administrator, may send or confirm the occurrence of anevent at appropriate frequencies.

In some embodiments, a smart contract or other symbolic AI system mayinclude a set of scores representing one or more properties of anentity. A smart contract may include an entity with a set of associatedentity score values representing various properties of the entity. Forexample, a smart contract may include an entity representing a powerfacility. The entity may include or be associated with a first entityscore indicating a maximum available power, a second entity scoreindicating an amount of transferable power, a third score indicating afacility fuel consumption rate, and a fourth score indicating a totalfailure risk.

In some embodiments, an entity may include or be associated withadditional entity properties. For example, a smart contract may includean entity representing a first asset. The first entity may include orotherwise be associated with a first entity property that includes afirst list of indicators, where each of the first list of indicatorspoints to an owner of the first asset. The asset may include orotherwise be associated with a second entity property that includes asecond list of indicators, where each of the second list of indicatorspoints to another entity owned by the first entity. In addition, theasset may also be associated with quantitative score values, such as anasset valuation, a monthly cash flow, a liability value, or the like.

In some embodiments, the value of an asset or otherwise associated withthe asset may be stored in various ways. For example, a scorerepresenting the value may be directly stored in a smart contract statedata instead of being included as a part of entity data. For example, asmart contract score may be stored in the knowledge list 250 of thesmart contract state data 200 described above, where the smart contractscore may represent the value of all entities listed assets by the smartcontract state data 200.

The box 1710 shows a directed graph including a set of norm vertices1711-1713 representing a set of consecutive obligation norms. Forexample, the set of norm vertices 1711-1713 may be associated with a setof consecutive obligation norms to repay a loan across three intervalsof time. The box 1710 also includes a norm vertex 1720 associated withan affirmative obligation norm, where an affirmative obligation norm maybe associated with an affirmative covenant on the part of a first entityto provide a data payload or digital asset at a pre-determined time, andwhere a failure to satisfy the conditions associated with the normvertex 1720 may result in activation of the obligation norm of the normvertex 1723. For example, the norm vertex 1720 may represent anaffirmative obligation norm that tests whether the first entity provideduser verification values to a second entity, where the verified data mayinclude information such as application health report, a financialstatement, a transaction confirmation, or the like. Failure to satisfythe conditions of the norm vertex 1720 before a failure state of thenorm vertex 1720 is reached may result in activation of the norm vertex1723, which may be associated with an obligation to transmit a messageor may result in the system autonomously generating a warning message.In some embodiments, a symbolic AI model may include a plurality ofaffirmative obligation norms associated with affirmative covenants. Forexample, some embodiments may include a first norm that includes normconditions to determine whether certified financial statements are sentfrom a first entity to a second entity at different times. The box 1710also includes a norm vertex 1730 associated with a prohibition norm,where the prohibition norm may include conditions and outcomes enforcingthe terms of a negative covenant. For example, the prohibition norm mayencode or otherwise be associated with a norm condition to determinewhether any entity made acquisitions greater than a pre-determinedthreshold or incur certain types of debt. In some embodiments, thesenorm conditions may be satisfied based on an automated report providedto an API of the smart contract. Alternatively, or in addition, thesenorm conditions may be satisfied based on an administrator action.

The box 1740 shows a directed graph of an outcome program state that mayfollow the program state shown by the directed graph of box 1710. Insome embodiments, one or more operations described above for the process1400 or the process 1500 may be used to determine an outcome programstate shown by the directed graph of the box 1740, where the initialprogram state may be represented by the directed graph of the box 1710.For example, the intelligent agent described above may determine theoutcome program states represented by the box 1740 and assign it anoutcome score of 85% using one or more operations described above.

As indicated by the directed graph in the box 1740, a condition of theprohibition norm of the norm vertex 1730 may be satisfied, which may inturn cause the computer system to generate an obligation norm associatedwith the rights norm of the fourth norm vertex 1731. In someembodiments, the rights norm of the fourth norm vertex 1731 may encodeor otherwise be associated with conditions based on whether acounterparty entity transmits a message including instructions toexercise the rights norm of the fourth norm vertex 1731. In addition, anevent has triggered the norm vertex 1720, where the event is a failureto satisfy the norm vertex 1720 before a failure time threshold issatisfied. The failure of the norm associated with the norm vertex 1720activates the obligation norm associated with the norm vertex 1723. Asecond event has then triggered the obligation norm associated with thenorm vertex 1723 by satisfying the norm condition of the obligationnorm, resulting in the satisfaction norm 1735. In some embodiments, thedirected graph may explicitly include the satisfaction norm 1735, suchas in a data structure storing the directed graph. Alternatively, or inaddition, the directed graph may generate an indicator indicating thatthe norm vertex 1723.

The box 1770 shows a directed graph representing a program state thatmay follow the program state shown by the directed graph of box 1740. Insome embodiments, one or more operations described above for the process1500 or the process 1600 may be used to determine an outcome programstate shown by the directed graph of the box 1740, where the initialprogram state may be represented by the directed graph of the box 1710.As indicated by the directed graph in the box 1770, the rights norm ofthe fourth norm vertex 1731 may be triggered, activating a newobligation norm associated with the norm vertex 1732. In someembodiments, the new obligation norm may include norm conditions todetermine whether a first entity transmits a payment amount to thesecond entity. For example, the new obligation norm may determinewhether the first entity transmitted the entirety of a principal paymentof a loan to the second entity. An event may then either satisfy or failthe norm condition encoded by or otherwise associated with the normvertex 1732. The satisfaction of the norm vertex 1732 may result in theactivation of the satisfaction norm vertex 1735, and failure of the normvertex 1732 may result in the activation of the failure norm vertex1737. In some embodiments, the satisfaction norm vertex 1735 or failurenorm vertex 1737 may be terminal vertices. Alternatively, in someembodiments, the norm vertex 1732 may be considered a terminal vertexfor embodiments where satisfaction norm vertices or failure normvertices are not explicitly stored in a directed graph.

FIG. 18 depicts a directed graph representing multiple possible programstates of a smart contract, in accordance with some embodiments of thepresent techniques. The directed graph 1800 may be generated based on asmart contract program state. The directed graph 1800 may represent aset of possible states for a set of possible events accounted for by asmart contract and may represent an expanded directed graph or acombination of expanded directed graphs. The directed graph 1800 may bestored in various forms in a data store. For example, the directed graph1800 may be stored in a form similar to that shown for the smartcontract state data 200. In some embodiments, operations to simulateevolving program state from an initial program state, such as thosedescribed above, may be used to determine the directed graph 1800 from aprevious directed graph. In some embodiments, a visual representation ofthe directed graph 1800 may be generated based on a symbolic AI modelsuch as a smart contract. In some embodiments, the visual representationmay include one or more user interface (UI) elements with which a usermay interact. Furthermore, the text shown in each of the vertices of thedirected graph 1800 may represent titles of specific vertices.

As shown in the directed graph 1800, the norm vertex 1801 includes thetitle “ra.” The “r” in “ra” may indicate that the norm vertex 1801represents a right norm. The “a” in “ra” may indicate that the normvertex 1801 is currently active. Combined, the name “ra” may indicatethat the norm vertex 1801 represents a rights norm that is currentlyactive. Each of the norm vertices 1811-1814 may be set as active oncethe rights norm represented by the norm vertex 1801 is triggered. The“op” in the title of each of the norm vertices 1811-1814 may indicatethat each of the norm vertices 1811-1814 represent obligation normswhich are set as active upon triggering the norm vertex 1801. Each ofthe norm vertices 1822-1835, 1841-1853, 1855, 1861, and 1862 may beassociated with a different norm. Each of the norm vertices that includea “s” in their title represents a satisfied norm. For example, the normvertex 1848 represents a satisfied norm, which indicates that the rightsnorm represented by the norm vertex 1831 is satisfied. Each of the normvertices that include a “c” in their label represents a cancelled norm.For example, the norm vertex 1845 represents a cancelled norm, whichindicates that the rights norm represented by the norm vertex 1827 iscancelled. Each of the norm vertices that include a “f” in their labelrepresents a failed norm. For example, the norm vertex 1845 represents acancelled norm, which indicates that the rights norm represented by thenorm vertex 1827 is cancelled.

In some embodiments, each of these norm vertices may have associateddata that may indicate a smart contract state at that position and maybe used to determine reward values. For example, a system may traversethe directed graph 1800 by first simulating a first entity initializinga smart contract represented by the directed graph 1800 by triggeringthe norm represented by the norm vertex 1801 to activate the obligationnorm represented by the norm vertex 1812. The computer system may thensimulate a second entity not satisfying the first obligation, resultingin the activation of the norms represented by the norm vertex 1825 and1827. The computer system may then simulate proceeding to the normrepresented by norm vertex 1844, resulting in the cancellation of rightsobligation represented by the norm vertex 1825 and the cancellation ofthe smart contract as a whole. The computer system may traverse a set ofpossible paths allowed by the edges of the directed graph 1800 todetermine score changes for each entity and use the score changes todetermine reward values associated with events, actions, vertices, orprogram states for one or more entities.

In some embodiments, each of the norm vertices 1841-1855 may represent aterminal norm, such as a cancelled norm or a satisfied norm. One or moreevents may trigger a set of norms to activate one or more new norms. Forexample, the satisfied norm represented by the norm vertex 1855 may besatisfied by any one of events represented by v1, v2, v3, or v4occurring. Furthermore, a norm may be triggered by an event to activatea plurality of other norms. For example, the occurrence of the eventindicated by the symbol “˜p5” may trigger the norm vertex 1811 toactivate the norm vertices 1822-1823, where the event “˜p5” may indicatethat the event “p5” did not occur before a failure time thresholdencoded by or otherwise associated with the norm vertex 1811 issatisfied.

FIG. 19 depicts a tree diagram representing a set of related smartcontract programs, in accordance with some embodiments of the presenttechniques. The box 1901 represents a first smart contract titled “SCA”having a score value of 760. The score value may represent one or moretypes of information. For example, the score value may indicate anamount of data in gigabytes to be transmitted by Entity A to one or moreother entities. The smart contract A may then be used to generate thechild smart contracts titled “SCA-1” and “SCA-2,” where the child smartcontracts may be labeled as child smart contracts with respect to SCA.Likewise, some embodiments may label or otherwise indicate SCA as aparent smart contract with respect to SCA-1 and SCA-2. In someembodiments, the newly-generated smart contracts may represent acontract rollover event sent by Entity A, wherein the event triggers arights norm of SCA to roll over SCA into the two smart contracts titled,SCA-1 represented by the box 1910 and SCA-2 represented by the box 1960.As shown, the score value associated with Entity A may also be dividedbetween SCA-1 and SCA-2.

As indicated by the arrow 1981 pointing from the box 1980 to the box1982, the smart contract SCA-2 represented by the box 1980 may be usedto generate the smart contract SCA-2.1, which may be labeled as a childsmart contract of SCA-2. Similarly, the arrow 1983 pointing from the box1982 to the box 1984 may indicate that SCA-2.1 may be used to generatethe smart contract SCA-2.1.1 represented by the box 1984. As indicatedby a comparison of the score values displayed by the boxes 1980-1984,the score values associated with an entity may remain unchanged when aparent contract is deprecated and a child smart contract is initiated.

As indicated by the arrow 1915 pointing from the box 1910 to the box1920, the smart contract SCA-1 represented by the box 1920 may be usedto generate the smart contract SCA-1.1, which may be indicated to be achild smart contract of SCA-1. Similarly, the arrow 1970 pointing fromthe box 1910 to the box 1971 may indicate that SCA-1 may also be used togenerate the smart contract SCA-1.2 represented by the box 1971. Asshown by the score values displayed in the boxes 1910, 1920, and 1971,the score value 600 displayed by the box 1910 may be distributed intothe score value 440 associated with SCA-1.1 and the score value 150associated with SCA-1.2, where a score change of 10 may occur via atransaction between Entity A to another entity during the generation ofthe smart contract SCA-1.1.

The smart contract SCA-1.1 may then be used to generate the smartcontract SCA-1.1.1, SCA-1.1.2, and SCA-1.1.3, as indicated by boxes1922, 1931, and 1941, respectively. The score value of 440 displayed bythe box 1920 and associated with SCA-1.1 may be distributed across thechild smart contracts of SCA-1.1. For example, as shown by the boxes1922, 1931, and 1941, the score value 440 associated with smart contractSCA-1.1 may be divided into the score values 20, 30, and 380,respectively. Furthermore, as indicated by the arrow 1921, the scoreassigned to SCA-1.1.1 is reduced by 10 during the generation ofSCA-1.1.1, which reduces the score associated with SCA-1.1.1 from 30 to20.

In some embodiments, each of the newly-generated contracts may beintegrated into an object or database of the first contract SCA.Alternatively, either or both of the newly-generated smart contracts maybe independent of the first contract, where each respective smartcontract may or may not have a link associating the child smart contractwith the parent smart contract. For example, the smart contract SCA-1.1may include a saved property titled “parent rollover smart contract”with a field value set as to “SCA.”

In some embodiments, a party may be an entity of a child smart contracteven if it is not an entity of a parent smart contract. For example,entity B may be the initial entity to which entity A is obligated toprovide a total data payload equal to 760 terabytes. After entity Bexercises a rights norm to generate SCA-1 and SCA-2, entity A may beobligated to allocate 600 terabytes of data for entity B and 160terabytes of data for a new entity C. Entity B may then further exercisea rights norm to generate SCA-1.1 and SCA-1.2 to offload memoryallocation instructions, where SCA-1.1 includes an obligation norm forentity A to allocate 440 terabytes to entity B and allocate 150terabytes for a new entity D. In addition, potentially as a condition ofgenerating additional memory allocation smart contracts on behalf ofentity B, the amount of memory that entity A is to allocate for entity Bmay be reduced by 10.

In some embodiments, the directionality of a graph edge of a directionalgraph may be used to indicate a cause and outcome. For example, the tailvertex of a graph edge may be associated with a first norm and the headvertex of the graph edge may be associated with a second norm that isactivated the first norm is triggered. In some embodiments, therelationship between triggering and triggered norm may be reversed,where the head vertex of a graph edge is associated with a first normand the tail vertex of the graph edge is associated with a second normthat is activated the first norm is triggered. The directionality of agraph edge as used herein is described for illustrative purposes and maybe used in different embodiments to denote different relationshipsbetween a triggering vertex and its outcome vertex. Also as used herein,the use of the article “a,” with respect an object does not necessitatea new instantiation or version of the object. For example, a directedgraph may expand over time from having 1000 vertices and 800 edges tohaving 2000 vertices and 1800 edges and can be referred to either “the”directed graph and “a” directed graph without any ambiguity. Inaddition, a data element, data object, data type, or data structure suchas a graph does not need to called a specific type in order to beconsidered a category associated with that type, and may be consideredto be of that type if it includes elements or programmed relationshipsof that type. For example, a graph does not need to labeled as adirected graph to be considered as a directed graph, and a set ofelements stored in a computer memory labeled “Terms Network” or someother title may be considered to be a directed graph if it includescomponents defining a directed graph, such as vertex indicesrepresenting graph vertices and a set of ordered pairs of vertex indicesindicating edges of the directed graph.

Modifying the functions of a smart contract program or other symbolic AIprogram after the program has begun executing may pose challenges indistributed computing environments. For example, a set of criteria of adistributed computing platform and the time required to transfer thedata needed to implement a modification in each local memory of adistributed computing platform may significantly increase the cost ofoperating a smart contract program on a distributed computing platform.While such computational costs may be advantageous by increasing thesecurity of a transaction and making tampering attempts evident, theymay inhibit the responsiveness of smart contract programs or othersymbolic AI programs operating on a distributed computing platform. Forexample, some distributed computing platforms may require more than oneminute, more than five minutes, or more than ten minutes to verify anddistribute an update to a smart contract program. The concurrentexecution of more than 10, more than 100, more than 1000, or more than100,000 operations to amend smart contract programs on a distributedcomputing platform may cause network or computing performance losses.Such network or computing performance losses may reduce the reliabilityof smart contract programs or make them less responsive to futureevents. Operations or related systems that reduce the cost of amending asmart contract program may increase the responsiveness of a smartcontract program to future events or verifiable changes.

Some embodiments may obtain an amendment request and extract one or morevalues from the amendment request usable to update a smart contractprogram state or other symbolic AI program state. The values extractedfrom the amendment request may include a set of conditional statementparameters, a set of entity identifiers, a set of conditional statementidentifiers, or the like. The values extracted from the amendmentrequest may be used to select or update a set of target norm vertices ofa smart contract program directed graph. Some embodiments may determine,based on the amendment request, one or more types operations to performsuch as updating a set of conditional statements, updating a set of normvertices, updating a set of entities, or the like, where updating a setmay include generating, modifying, or deleting an element of the set.

Some embodiments may simulate the updating of a directed graph based onone or more conditional statement identifiers encoded in the amendmentrequest and modify an amendment request based on the simulation. Forexample, some embodiments may simulate a state change caused by anamendment request and then determine an outcome program state based onthe simulated program state change. Based on a comparison of an outcomeprogram state value with a threshold (e.g., a threshold provided by averification agent), some embodiments may prevent the amendment requestfrom changing the program state or modify instructions of the amendmentrequest to satisfy the threshold.

Some embodiments may use a simulation of a state change caused by anamendment request to determine whether a modified smart contract programstate would satisfy a set of criteria of one or more of the entities ofa smart contract program, where the directed graph of the smart contractprogram is stored in a deserialized form on a first computing device. Inresponse to a determination that the set of criteria is satisfied, someembodiments may modify the smart contract program state based on theamendment request and serialize the deserialized directed graph into aserialized array for distribution to other computing devices of adistributed computing platform. By deserializing and reserializing adirected graph based on an amendment request, some embodiments mayreduce the memory needs of each computing device of the distributedcomputing platform. Furthermore, such operations may allow someembodiments may reduce the load on a network used to operate adistributed computing platform. However, while some embodiments mayperform one or more of the above-recited operations, these operationsare not necessary for some embodiments, and some embodiments may foregosuch operations to reduce processor use or to enjoy other advantages.

Some embodiments may select a set of entities based on the set of targetnorm vertices and determine whether a set of criteria of each selectedentity of the set of selected entities is satisfied. Some embodimentsmay use the modified smart contract program state described above todetermine whether the set of criteria are satisfied. Some embodimentsmay send a message to each respective entity of the set of selectedentities, where the message may indicate that the respective entity is aparticipant of a target norm vertex. Alternatively, or in addition, someembodiments may include a requirement that a confirmation messageauthenticating the acceptance of the amendment request from eachrespective entity of the set of selected entities is obtained.

Some embodiments may update a smart contract program state in responseto determining that the set of criteria are satisfied, such as byupdating a set of conditional statements, updating their associatedtarget norm vertices, updating their associated entities, or the like.Some amendment requests may further cause some embodiments to associatea newly-generated directed graph portion or an existing directed graphportion to one or more target norm vertices. Some embodiments may assignpriority category values to one or more norm vertices, where thepriority category values may be used to determine an order of vertexactivation or vertex triggering in the case of a single event triggeringmultiple norm vertices.

FIG. 20 depicts an example representation of an amendment requestmodifying a directed graph of a smart contract program, in accordancewith some embodiments of the present techniques. The directed graphsshown in FIG. 20 are visualized in the form of boxes and edges. However,other representations are possible, and the directed graphs be stored incomputer memory in various formats, such as arrays, matrices, dataobjects, or the like. The dashed box 2010 encloses a directed graphassociated with a smart contract program state having a first normvertex 2012 that may be categorized as an obligation norm. A conditionalstatement of the first norm vertex 2012 may include a condition that issatisfied if a first entity used to fill the entity field P1 allocates afirst quantity equal to 100 units to a second entity used to fill theentity field P2. In some embodiments, the conditional statement of thefirst norm vertex may encode the first entity having the entityidentifier “Ent1” and the second entity having the entity identifier“Ent2” based on the entity identifiers filling their respective entityfields of the conditional statement. Alternatively, or in addition, someembodiments may encode the first and second entity by hardcoding theirrespective entity identifiers into them. Additionally, some embodimentsmay determine that a norm vertex is associated with a set of entitiesencoded in a conditional statement associated with first norm vertex2012. For example, some embodiments may determine that the first normvertex 2012 is associated with the first entity and second entitybecause their corresponding entity identifiers are encoded in aconditional statement that is associated with first norm vertex 2012.

As indicated by the first directed graph edge 2013, failing to satisfythe conditional statement of the first norm vertex 2012 may result inthe activation of the second norm vertex 2014, which may be categorizedas a rights norm. The rights norm represented by the second norm vertex2014 may represent a right of the first entity “Ent1” to allocate 105units to the second entity “Ent2.” In some embodiments, while not shown,satisfaction of the first norm vertex 2012 or second norm vertex 2014may cause the activation of a norm vertex having no further childvertices that indicate that the obligation is fulfilled. Alternatively,or in addition, some embodiments may update a label to indicate that thenorm vertex satisfied and has no additional child vertices. As indicatedby the second directed graph edge 2015, failing to satisfy theconditional statement of the first norm vertex 2012 may also result inthe activation of a third norm vertex 2016 categorized a rights norm.The third norm vertex 2016 may represent a right of the second entity“Ent2” to force the allocation of an amount represented by the value“TTL” by the first entity “Ent1,” where “TTL” may be variable numberdependent on a remaining amount. For example, the third norm vertex 2016may represent a right of the second entity “Ent2” to force theallocation of 100 GB of a non-duplicable resource by the first entity“Ent1.”

The smart contract program state associated with the directed graph 2010may be modified by the amendment request 2030. The amendment request2030 may include various parameters. For example, the amendment request2030 may include a date on which the amendment is to take effect, aconditional statement identifier indicating the conditional statementthat the amendment request 2030 is to modify or replace, or a pair ofmodifying conditional statements. The first modifying conditionalstatement of the pair of modifying conditional statements may indicatethat the first entity “Ent1” is to transfer 10 units to a third entity“Ent3.” A second modifying conditional statement of the pair ofmodifying conditional statements may indicate that the first entity isto transfer 89 units to the second entity “Ent2.” As discussed furtherbelow, some embodiments may extract conditional statement parameterssuch as the entity identifiers, the quantitative amounts, the date, andthe conditional statement identifier.

The directed graph 2040 may be an outcome directed graph after a statechange to the program state of the directed graph 2010, where the statechange is caused by the amendment request 2030. The directed graph 2040includes a first directed graph portion 2050 and a second directed graphportion 2060, where the first directed graph portion 2050 and the seconddirected graph portion 2060 are disconnected from each other. The firstdirected graph portion 2050 includes the first norm vertex 2012, wherethe entity identifier “Ent3” replaces the entity identifier “Ent2” forthe entity field “P2.” In some embodiments, conditional statements ofadjacent norm vertices may be affected based on the amendment request,even if the adjacent norm vertex is not directly referenced by anamendment request or does not use a conditional statement identified byan amendment request. For example, the second norm vertex 2014 may beupdated such that the quantity “105” is changed to the quantity “94”based on the quantity of the rights norm the set as equal to the sum ofthe first quantity and an additional five units. Additionally, the thirdnorm vertex 2016 may be updated such that the quantity “TTL” is changedto the quantity “(TTL-10).” Furthermore, some embodiments may include ascore change, transfer of scores, or allocation of resources as a resultof modifying a smart contract program based on the amendment. Forexample, an amendment request may cause some embodiments to cause thefirst entity to directly allocate an hour of processor time to a fourthentity.

The directed graph 2040 includes the second directed graph portion 2060,where the second directed graph portion may include a fourth norm vertex2061, a fifth norm vertex 2063, a sixth norm vertex 2065, a seventh normvertex 2067, an eighth norm vertex 2069, and a ninth norm vertex 2071. Aconditional statement of the fourth norm vertex 2061 may include acondition that is satisfied if a first entity used to fill the entityfield P1 allocates a first quantity 10 units to a second entity used tofill the entity field P2, where the first entity has the entityidentifier “Ent1,” and the second entity has the entity identifier“Ent3.” As indicated by the third directed graph edge 2062, failing tosatisfy the conditional statement of the fourth norm vertex 2061 mayresult in the activation of the fifth norm vertex 2063. The rights normrepresented by the fifth norm vertex 2063 may represent a right of thefirst entity “Ent1” to allocate 11 units to the second entity “Ent3,”which may be interpreted as curing a failure to satisfy the fourth normvertex 2061. As indicated by the second directed graph edge 2064,failing to satisfy the conditional statement of the fourth norm vertex2061 may also result in a third rights norm represented by the sixthnorm vertex 2065. The third rights norm represented by the sixth normvertex 2065 may represent a right of the second entity “Ent3” to forcethe allocation of an amount represented by the variable TTL/10 by thefirst entity “Ent1.” Furthermore, as indicated by the directed graphedge 2072, failing to satisfy the conditional statement of the sixthnorm vertex 2065 may result in the activation of the seventh norm vertex2073. An obligations norm represented by the seventh norm vertex 2073may represent an obligation of the second entity “Ent2” to allocateeight units to the third entity “Ent3.”

In some embodiments, the process 2100 of FIG. 21, like the otherprocesses and functionality described herein, may be implemented ascomputer code stored on a tangible, non-transitory, machine-readablemedium, such that when instructions of the code are executed by one ormore processors, the described functionality may be effectuated.Instructions may be distributed on multiple physical instances ofmemory, e.g., in different computing devices, or in a single device or asingle physical instance of memory (e.g., non-persistent memory orpersistent storage), all consistent with use of the singular term“medium.” In some embodiments, the operations may be executed in adifferent order from that described, some operations may be executedmultiple times per instance of the process's execution, some operationsmay be omitted, additional operations may be added, some operations maybe executed concurrently and other operations may be executed serially,none of which is to suggest that any other feature described herein isnot also amenable to variation.

FIG. 21 is a flowchart of a process to modify a program state based onan amendment request, in accordance with some embodiments of the presenttechniques. In some embodiments, the process 2100 may include obtaininga smart contract program or other symbolic A program encoding anassociated directed graph, as indicated by block 2104. Obtaining a smartcontract program state encoding an associated directed graph may includeloading data from, copying a version of, or otherwise accessing a smartcontract program or other symbolic AI program. In some embodiments, thesmart contract program state may be active and in the process of beingexecuted by a computing system and include data stored in persistentmemory or non-persistent memory. For example, some embodiments mayobtain the smart contract program state by executing the smart contractprogram state and retrieving a program state encoding the associateddirected graph of the smart contract program state. Alternatively, or inaddition, the smart contract program state may be archived or otherwisestored in a persistent memory of a computing system. The smart contractprogram state may be obtained from a smart contract program state, apredicted future state, a simulated state, or the like.

In some embodiments, as described above, the smart contract programstate may encode a directed graph in the form of a serialized array ofnorm vertices and its corresponding set of graph edges. For example, thesmart contract program state may include a serialized array of normvertices “[1, 4, 7],” where each number of the numbers indicates a normvertex, and a corresponding set of directed graph edges “[[1,4],[4,7]],” where each subarray indicates a directed graph edge.Alternatively, or in addition, the smart contract program state mayinclude a plurality of serialized arrays of norm vertices and theircorresponding edges. For example, the smart contract program state mayinclude a first serialized array of norm vertices “[1, 4, 7]” associatedwith a corresponding serialized array of directed graph edges [[1, 7],[7,4]]. The smart contract program state may include a second serializedarray of norm vertices “[2, 5, 7, 4, 41]” and their correspondingdirected graph edges [[2,5], [7,5], [5,4], [4,41]]. As discussed furtherbelow, storing portions of a graph in separate serialized arrays of normvertices may contribute to increasing memory efficiency or updateefficiency when executing a smart contract program state.

In some embodiments, the directed graph may be disconnected, having twoor more unconnected portions. For example, the smart contract programstate may include a first serialized array of norm vertices “[1, 4, 7]”associated with a corresponding serialized array of directed graph edges[[1, 7],[7,4]]. The smart contract program state may also include asecond serialized array of norm vertices “[2, 5, 7, 4, 41]” and theircorresponding directed graph edges [[2,5], [4,5], [4,41]], where thegraph portion formed by the first serialized array of norm vertices isdisconnected from the graph portion formed by the second serializedarray of norm vertices.

In some embodiments, the process 2100 may include obtaining an amendmentrequest encoding a set of conditional statement parameters, as indicatedby block 2108. The amendment request may be obtained at an API of asmart contract program, API of application in communication with thesmart contract program, an API of a distributed computing platform, anAPI of a computing system executing the smart contract program, or thelike. Some embodiments may obtain the amendment request from dataentered by a user at a graphical user interface. Alternatively, or inaddition, some embodiments may obtain an amendment request that wasmachine-generated or updated using a machine-learning system. The set ofconditional statement parameters may include various types ofinformation, such as a date of enforcement, a quantitative value, acategorical value, an entity identifier or other identifier, or thelike. For example, an amendment request corresponding to the naturallanguage instructions “entity 12591xc3 is obligated to allocate 300 GBto entity 27831t6” may include the quantity parameter “300” and theentity identifiers “12591xc3” and “27831t6.”

The set of conditional statement parameters may include some or all of aconditional statement written in a computer-readable programminglanguage. For example, the set of conditional statement parameters mayinclude the conditional statement “if (ENTITY==“entity1x1”):SENDRESOURCE(100, “entity1x1”, “entity1x2”)”. Alternatively, or inaddition, the set of conditional statement parameters may include a setof parameter values used to fill a field a conditional statement. Forexample, the set of conditional statement parameters may include aconditional statement identifier “cond_state10105421” with theassociated parameters [“entity1x1”; “entity1x2”; 194]. The conditionalstatement identifier “cond_state10105421” may identify a conditionalstatement that may be represented in the form “if RCVD_FUNCTION(ARG1,ARG2, ARG3).” The function “RCVD_FUNCTION” may be a function thataccepts the three parameters ARG1, ARG2, and ARG3 and returns theboolean “true” if ARG1 and ARG2 are entities and ARG1 has received theamount ARG3 from the entity identified by ARG2. The associatedparameters may indicate that “entity1x1” is to be used in place of ARG1,that “entity1x2” is to be used in place of ARG2, and that “194” is to beused in place of ARG3.

In some embodiments, the amendment request may include specificinstructions to generate a new directed graph portion. For example, theamendment request may include the instructions “Generate_Vertex(1525,215, “satisfied,” 15216),” which may cause a smart contract program togenerate a norm vertex identified as “1525.” A graph edge may associatethe newly-generated norm vertex to the norm vertex identified as “215”and be activated based on the satisfaction of the norm vertex identifiedas “215.” The final parameter of the function “Generate_Vertex” mayindicate that the newly-generated norm vertex may be associated with aconditional statement identified as “15216.” Alternatively, or inaddition, some embodiments may determine that an amendment requestincludes instructions or values satisfiable by the generation of a newdirected graph portion and, in response, generate the new graph portion.For example, some embodiments may determine that the instructionsinclude “if vert_failed(vert[1653]): cond_state[15216],” where theinstructions may cause a smart contract program to determine that a normvertex associated with the conditional statement “15216” is inexistence.

In some embodiments, the process 2100 may include determining a set oftarget norm vertices of the smart contract program state or othersymbolic AI system based on the amendment request, as indicated by block2116. In some embodiments, the amendment request may include directreferences to one or more norm vertices of a directed graph of a smartcontract program. For example, the event request may include a referenceto the norm vertex “OP111-1,” where the value “OP111-1” is a norm vertexidentifier for a norm vertex in a smart contract graph. Alternatively,the amendment request may include a series of the old parameters toidentify one or more smart contract graph norm vertices. For example,some embodiments may receive an amendment request that includes a set ofnorm vertex-identifying parameters, such as a parameter that specifies atransaction date, a set of affected entities, or a transaction amount.Some embodiments may extract the conditional statement parameters orother norm vertex-identifying parameter from the amendment request,where a norm vertex-identifying parameter may be any value that can beused to identify a set of norm vertices. For example, a normvertex-identifying parameter may include a norm vertex identifier, acategory type specific to a set of norm vertices, a time or timeinterval that can be used to isolate a set of norm vertices havingfulfillment deadlines due after the time or within the time interval, orthe like. Each parameter of the set of norm vertex-identifyingparameters may then be used by a computing system to determine one ormore norm vertices or conditional statements. For example, an amendmentrequest specifying a modification for all transactions associated with aconditional statement identifier after a specified date may result inthe extraction of the conditional statement identifier and the specifieddate as norm vertex-identifying parameters.

Some embodiments may determine a set of active norm vertices of adirected graph of a smart contract program or other symbolic AI program.Some embodiments may search through the set of active norm verticesinstead of searching through all norm vertices of the directed graph. Insome embodiments, an active norm vertex may be a norm vertex having anassociated conditional statement that may be triggered, whereupon thetriggering of the associated conditional statement causes the activationof another norm vertex to take place or may otherwise cause an statechange to occur (e.g., update a label indicating that an obligation normis satisfied). For example, some embodiments may determine that a first,second, and third norm vertex are each active norm vertices of a set ofactive norm vertices. Some embodiments may then search through the setof active norm vertices that satisfy a set of norm vertex-identifyingparameters to determine a target norm vertex. For example, an amendmentrequest may include norm vertex-identifying parameters specifying achange to terms associated with an allocation of 100 units by a firstentity for a second entity. Some embodiments may extract the firstentity identifier, the second entity identifier, and the allocation of100 units to form a set of norm vertex-identifying parameters and searchthrough a set of active norm vertices to find which norm verticessatisfy the set norm vertex-identifying parameters. This is not tosuggest that all embodiments may restrict the search to a set of activenorm vertices, and some embodiments may search through other normvertices of a directed graph or all norm vertices of a directed graphfor their own benefit(s), such as for ensuring a completeness of thesearch or for providing data for a study of historical performance.

Some embodiments may obtain amendment requests that may modify one ormore conditional statements and search for a list of affectedconditional statements, which may then be used to select a target normvertex. For example, some embodiments may include an amendment requestthat includes the computer-interpretable code ‘if entity[2123].sending()==true, Replace(2123, 254121),’ which may be converted from the naturallanguage statement, “if the entity having the entity identifier ‘2123’is sending a resource, replace it with the entity having the entityidentifier ‘254121.’” Some embodiments may then search through a set ofconditional statements to determine which of the set of conditionalstatements are affected by the amendment request based on which entitiesare part of a condition of the conditional statement. For example, afirst conditional statement may include the condition ‘ifentity[2123].sending( )==true: message(entity[254121], “SENT”).’ Someembodiments may determine that the first conditional statement is anaffected conditional statement and then determine a set of target normvertices that includes, uses, or is otherwise associated with the firstconditional statement.

In some embodiments, the process 2100 may include determining a set ofselected entities based on the set of target vertices, as indicated byblock 2120. As discussed above, the set of target vertices may beassociated with a set of entities via the set of conditional statementsencoding one or more of the set of entities. This set of entities may beused as a set of selected entities, where the set of selected entitiesmay indicate entities that are directly affected by the amendmentrequest. By determining which entities are directly affected by anamendment request, some embodiments may more effectively message the setof affected entities without overloading a messaging system by notifyingonly entities that are likely to have an interest in the amendmentrequest. For example, a smart contract program may include a list of 100entities, amongst which 25 are determined as being affected by anamendment request based on the entities associated with verticesaffected by the amendment request. These 25 entities may then be sent afirst message indicating that they are affected by the amendmentrequest, while the other 75 entities are not sent any messages or sent amessage different from the first message. Furthermore, some embodimentsmay require that an amendment request be confirmed by the set ofselected entities instead of requiring that confirmation be provided byall entities of a smart contract program. By reducing the number ofrequired confirmation messages, some embodiments may significantlyreduce the time needed to amend a smart contract program having morethan ten entities, more than twenty entities, more than two hundredentities, or the like by requiring a confirmation message from only asubset of the entities of the smart contract program.

In some embodiments, the process 2100 may include simulating amodification of the smart contract program state or other symbolic AIprogram state based on the amendment request, as indicated by block2124. Some embodiments may simulate the modification of an applicationprogram state based on the amendment request by generating a version thesmart contract program state and changing the version based on theamendment request without requiring the distribution of the changedversion to other computing devices. Simulating a modification of thesmart contract program state may include generating a new graphstructure that is different from the graph structure of an unmodifiedsmart contract program state with respect to the number of normvertices, the number of edges, or the set of logical categoriesassociated with each of the norm vertices. For example, a first graphstructure of unmodified smart contract program state may include threeactive obligation norm vertices. After simulating a modification basedon the amendment request, the graph structure of the modified smartcontract program state may be changed such that the logical category ofone norm vertex is changed from being an obligation norm to aprohibition norm. This may cause the simulated modification of the graphstructure to include two active obligation norm vertices and one activeprohibition norm vertex. Simulating a modification of the smart contractprogram state may include generating a version of a set of conditionalstatements and updating the version of the set of conditional statementsbased on the set of conditional statement parameters.

As further discussed in this disclosure, a simulated modification basedon an amendment request may be used to determine whether an outcomestate caused by the amendment request satisfies a set of requirements ofthe smart contract program. For example, as further illustrated below,an amendment request to a smart contract program state may cause a firstentity to be obligated to allocate five terabytes of memory to a secondentity. Some embodiments may simulate an implementation of the amendmentrequest and determine that the simulated outcome state violates a thirdentity's requirement. For example, the third entity's requirement may bethat the second entity is prohibited from reserving memory allocated bythe first entity, where third entity's requirement may be implemented asa prohibition norm or may be implemented in another form (e.g.,program-wide rule) encoded in the smart contract program. In response tothis simulated violation, some embodiments may prevent the amendmentrequest from modifying the smart contract program state or may modifythe instructions of the amendment request (e.g., by changing the sourceentity for the allocated memory).

Some embodiments may simulate the modification of an application programstate without simulating every modification caused by the amendmentrequest. For example, a first amendment request may cause themodification of a program state to change the conditional statement andlogical category of a first norm vertex. The change may cause anobligation norm of a first entity to allocate a first amount to a rightsnorm triggerable by a second entity to request a second amount from thefirst entity after an interval of time encoded in the amendment request.The computing system may simulate the modification of the applicationprogram state by changing the logical category associated with the firstnorm vertex and any associated graph structure changes without changingthe first amount to the second amount or include the encoded interval oftime. Based on a determination that a simulated modified graph structureis not identical to or otherwise different from a graph structure beforea smart contract modification, some embodiments may generate a new normvertex for a directed graph of a smart contract program.

In some embodiments, a representation of the simulated modification ofthe application program state based on the amendment request may be sentto one or more verification agents. A verification agent may include athird-party entity, an automated testing system, or another system. Forexample, some embodiments may simulate modification of a first smartcontract program and send the graph structure, their correspondingconditional statements, or other related data to a verification agentfor display a graphical user interface. Alternatively, some embodimentsmay simulate the modification of the application program state withoutany messages. The verification agent may send a message via an API,where the message may confirm that the simulated modification isacceptable, reject the simulated modification or may include a secondsimulated modification of the smart contract program state. As furtherdescribed below, some embodiments may require a message confirming theauthorization of the simulated modification before proceeding to use thesimulated modifications for additional analysis. Alternatively, someembodiments may use the simulated modifications for further analysiswithout requiring a confirmation message.

Some embodiments may simulate the occurrence of a set of simulatedevents or a sequence of simulated events for a simulated modified smartcontract program. For example, some embodiments may simulate theoccurrence of a sequence of simulated events indicated to have occurredbased on an associated set of occurrence times, where the associated setof occurrence times indicate the occurrence of a first simulated eventof the sequence of simulated events on a first day and the occurrence ofa second simulated event of the sequence of simulated events on a secondday. Some embodiments may simulate the occurrence of a plurality ofsimulated events or a plurality of sequences of simulated events todetermine a set of outcome scores corresponding to a set of simulatedmodified smart contract program states. For example, some embodimentsmay set the average amount of computing memory allocated over one monthas an outcome score and determine a set of outcome program states acrossthree equally-likely sequences of events based on five differentsimulated program states.

Some embodiments may determine a respective set of outcome programstates for each respective simulated program state of the five differentsimulated program states, where each respective simulated program stateis a result of modifying the program state based on a respectiveamendment request of the plurality of amendment requests. Someembodiments may then determine a respective set of outcome scores foreach respective set of outcome program states. Some embodiments may thenselect an amendment request from the plurality of amendment requestsbased on the set of outcome scores, where the set of outcome scores mayinclude each of the respective set of outcome scores. For example, someembodiments may select an amendment request based on which results in amaximum outcome score of the set of outcome scores. Some embodiments mayperform one or more of the simulation operations described above using asingle computing device or subset of computing devices of a distributedcomputing platform instead of using each computing device of thedistributed computing platform to simulate a modification. By performingthe simulation on a one computing device or a small number computingdevices, some embodiments may reduce the computational load on thedistributed computing platform and reduce the network traffic used tooperate the distributed computing platform.

In some embodiments, the process 2100 may include determining whetherthe amendment request satisfies the set of criteria of the set ofselected entities, as indicated by block 2128. Determining whether theamendment request satisfy the set of criteria of the set of selectedentities may include determining whether the amendment request satisfiesa set of governing conditional statements associated with the smartcontract program. For example, a governing set of conditional statementsmay prohibit transactions with entities of a first entity type, and theamendment request may change a participating entity to an entity of thefirst entity type. In response, some embodiments may determine that theamendment request does not satisfy the set of criteria of the set ofselected entities. In some embodiments, each entity of the set ofselected entities may have a different set of criteria that must besatisfied in order to provide a respective confirmation message.

In some embodiments, the set of confirmation messages may include one ormore authentication frameworks to authenticate a confirmation message.For example, the set of confirmation messages may include a set ofpasskey values. For example, each respective message of a set ofconfirmation messages may include a respective passkey value of the setof passkey values, where each message of the set of confirmationmessages may be associated with a respective entity of the set ofselected entities. For example, an entity may send a confirmationmessage, including a human-entered or machine-provided passkey value.Some embodiments may compare a respective passkey value with arespective stored passkey value to determine whether the respectivepasskey value matches with the respective stored passkey value. In someembodiments, some embodiments, the passkey value may be encrypted andcompared to a set of encrypted passkey values to determine a match.Alternatively, or in addition, a passkey value may be decrypted andcompared to a set of decrypted passkey values to determine a match.Based on the entity-sent passkey value matching a stored passkey valuein a set of stored passkey value, some embodiments determine that acriterion associated with one or more of the set of selected entities issatisfied. While the above describes an implementation of one type ofauthentication framework, some embodiments may use one of various othertypes of authentication frameworks when sending confirmation messages orother messages. For example, some embodiments may implement a Public KeyInfrastructure (PKI) framework, such as that described in “Introductionto public key technology and the federal PKI infrastructure” (Kuhn, D.Richard, et al. National Inst of Standards and Technology GaithersburgMd., 2001), which is hereby incorporated by reference. Some embodimentsmay use various data transport protocols when implementing theauthentication framework, such as secure socket layer (SSL) or transportlayer security (TLS).

Various types of criteria may be used to determine whether to modify asmart contract program state based on the amendment request. In someembodiments, determining whether a criterion is satisfied may includedetermining whether an entity of a smart contract program is one of aset of prohibited entities or one of a set of prohibited entity types.For example, the entity “entity1” may have the entity type “x1x1,” anddetermining whether the set of criteria is satisfied may includedetermining that entities of the entity type “x1x1” are entities of aprohibited entity type. In response, some embodiments may determine thatthe set of criteria is not satisfied by the entity “entity 1.”

In some embodiments, determining whether the set of criteria issatisfied may include determining whether a non-duplicable asset isconcurrently transferred or allocated to different entities based on asingle event or sequence of events. A non-duplicable asset may includean amount of computing time on a specific computing resource during aspecific time interval. For example, a non-duplicable asset may includean allocated utilization time between 04:00 and 06:00 on a specified setof processor cores. The transfer or allocation of a non-duplicableresource to multiple resources may be detected as a conflict, and someembodiments may include a verification mechanism to prevent theconflict. For example, some embodiments may determine a simulatedcontract program state based on a simulation of the modification of asmart contract program state based on an amendment request. Someembodiments may then simulate how the simulated smart contract programstate responds to a sequence of events and determine that a first entityis allocating control of a specific computing resource to a secondentity based on an event, and that the first entity will also be causedto allocate control of the specific computing resource to a third entitybased on the event. Some embodiments may determine this concurrentallocation and, in response, prevent the amend request from beingimplemented, send a message indicating that the amendment may cause aconflict, or the like.

In some embodiments, the set of criteria may include determining that anentity, set of entities, or the entity type is required for transactionsof a specified transaction type or all transaction types. For example,some embodiments may include a criterion that each entity of a smartcontract program is indicated as verified based on a verification fieldbeing populated with the value “verified.” As another example, theentity “entity2” may have the entity type “x2x2” and a possiblecriterion may be that all entities of a smart contract program state beof the entity type “x2x2.” In response, some embodiments may determinethat the entity “entity2” satisfies the criterion.

Some embodiments may perform one or more of the determination operationsdescribed above using a subset of computing devices of a distributedcomputing platform instead of using each computing device of thedistributed computing platform to determine whether a set of criteriaare satisfied. For example, some embodiments may store a version of eachof a set of criteria of a set of entities at a storage memory. Someembodiments may then determine whether an amendment request satisfiesthe set of entities using a computing device that includes or isotherwise capable of accessing the storage memory. After determiningthat the set of criteria is satisfied, some embodiments may send theamendment request or parameters stored in the amendment request to othercomputing devices of the distributed computing platform. By restrictingthe determination operation to one computing device or a small number ofcomputing devices, some embodiments may reduce the overall computationalload on the distributed computing platform and reduce the networktraffic used to operate the distributed computing platform. In someembodiments, if the amendment request satisfies the set of criteria ofthe set of selected entities, operations of the process 2100 may proceedto block 2140. Otherwise, operations of the process 2100 may proceed toblock 2132.

In some embodiments, the process 2100 may include updating an amendmentrequest, as indicated by block 2132. In some embodiments, the amendmentrequest may be updated in response to a failure to satisfy the set ofcriteria of the selected entities. In some embodiments, the amendmentrequest being generated may be generated in a parameter space thatallows the amendment request to have multiple possibilities. In someembodiments, some of these possibilities of the amendment request maysatisfy the set of criteria of the selected entities while otherpossibilities of the amendment request May not satisfy the set ofcriteria of the selected entities. For example, a first version of therequest may change a first quantity from the value 100 to the value 300and a first criterion of one of the selected entities may require thatthe value of the first quantity the less than 200. In response, afterdetermining that the amendment request failed the first criterion, someembodiments may update the agreement request to change the firstquantity from the value 300 to the value 150 using one or more ofvarious types of optimization methods or machine-learning methods.

Some embodiments may send a message to one or more of the set ofselected entities in response to failing to satisfy the set of criteriaof the selected entities. For example, some embodiments may send amessage to all of the set of selected entities in response to failingthe set of criteria. Some embodiments may send a message indicating thatthe set of criteria has been failed without updating the amendmentrequest. Additionally, or alternatively, some embodiments may send amessage to an entity or other agent that sent the amendment requestindicating that the amendment request on a program state resulting fromthe amendment request has failed the set of criteria.

In some embodiments, the process 2100 may update a set of conditionalstatements based on the set of conditional statement parameters, asindicated by block 2140. In some embodiments, determining the updatedset of conditional statements may include replacing one or moreconditional statements with a new conditional statement determined fromthe set of conditional statement parameters. For example, a norm vertexmay be associated with a first conditional statement, where the firstconditional statement is indicated to be replaced by a secondconditional statement encoded in an amendment request. In someembodiments, a conditional statement that is to be replaced or otherwiseunused may be marked as deprecated. For example, some embodiments maychange a usage indicator associated with a conditional statement toindicate that the conditional statement is deprecated based on anamendment request indicating that the conditional statement should beremoved from use in the smart contract program. Alternatively, insteadof deprecating a conditional statement stored in a set of conditionalstatements, some embodiments may determine delete the conditionalstatements.

In some embodiments, updating the set of conditional statements mayinclude filling out, replacing, or otherwise using one or moreconditional statement parameters to populate fields of the set ofconditional statements. For example, the computing system may obtain afirst plurality of entity identifiers, and a user may use the entityidentifiers fill out the function that uses the first plurality ofentity identifiers. By filling out a field of an existing conditionalstatement instead of replacing the condition statement, some embodimentsmay increase the speed by which a smart contract program state may bedistributed on a distributed computing platform. However, while theabove describes filling, replacing, or otherwise using one or moreconditional statement parameters to populate fields of the set ofconditional statements, some embodiments may forego such operations andupdate the set of conditional statements using other methods.

In some embodiments, updating the set of conditional statements mayinclude adding one or more conditional statements to the set ofconditional statements. Some embodiments may determine an updated set ofconditional statements indexed by conditional statement identifiers byadding a new conditional statement that may have its own associatedconditional statement identifier. For example, an amendment request mayinclude an instruction to add a specific conditional statement to a setof conditional statements of a smart contract program. In response toobtaining the amendment request, some embodiments may includeinstructions to add a conditional statement identifier “x1x1” to aconditional statement identifier index.

In some embodiments, updating the set of conditional statements mayinclude determining whether a conditional statement (or an associatednorm vertex) was triggered by a past event. An outcome of theconditional statement may include a transaction between a first entityand a second entity. For example, after determining that a past eventhad triggered a norm vertex based on an amendment request modifying thenorm vertex or an associated conditional statement, some embodiments maydetermine a first score value associated with a transaction caused bythe outcome. Some embodiments may determine a second score value encodedin the amendment request and determine a difference between the firstscore value and the second score value. Some embodiments may theninitiate a transaction between the first entity and the second entitybased on the score difference. By using a score differences to accountfor differences between amendment requests and past events, someembodiments may include mechanisms to retroactively apply an amendmentrequest.

In some embodiments, updating the set of conditional statements, set oftarget vertices, or other values of a program state operating on adistributed computing platform may include sending a set of values toeach computing device of the distributed computing platform. In someembodiments, to determine the validity of a distributed value, the smartcontract program may use one or more consensus algorithms. For example,to reach a consensus on the validity of a set of conditional statements,some embodiments may use a consensus Paxos algorithm, a Raft algorithm,HotStuff, or the like. Furthermore, some embodiments may centralize oneor more of the operations described above at a single computing deviceor a subset of computing devices and then send a processed set of valuesto other computing devices of the distributed computing platform. Forexample, some embodiments may simulate modifications of a smart contractprogram using a first computing device and determine an updatedamendment request based on the simulated modifications before sendingthe updated amendment request to other computing devices. Additionally,some embodiments may store or otherwise have access to a set of criteriaof each entity of a set of selected entities to determine whether theset of criteria is satisfied. By centralizing operations at a singlecomputing device or a subset of computing devices, some embodiments mayreduce the overall computational cost of amending a smart contractprogram or other symbolic AI program.

In some embodiments, the process 2100 may include updating the set oftarget norm vertices or values associated with the set of target normvertices based on the amendment request, as indicated by block 2144.Updating the set of target norm vertices may include updating a field ofthe target norm vertex for a conditional statement identifier of thetarget norm vertex with a new conditional statement identifier. Updatingthe field may associate the target norm vertex with the new conditionalstatement having the new conditional statement identifier. For example,a first target norm vertex may have or otherwise be associated with theconditional statement identifier “4457” and updated to have instead orotherwise be associated with the conditional statement identifier“9941.” After the update, a first event satisfying the conditionalstatement having the identifier “9941” may trigger the first target normvertex, whereas a second event satisfying the conditional statementhaving the identifier “4457” does not trigger first target norm vertex.

Some embodiments may update the smart contract program state bydeserializing a serialized array representing a directed graph stored inpersistent memory. The deserialized directed graph may be stored innon-persistent memory for fast processing or operations. Someembodiments may then add, modify, or remove a norm vertex or edge of thedirected graph stored in the non-persistent memory and then reserializethe directed graph. For example, some embodiments may include a firstserialized array “[1 3 5]” having an associated serialized array ofedges “[[1,3], [3,5]]” stored in a persistent memory of a computingsystem. Some embodiments may deserialize the serialized array into anadjacency matrix form stored in the non-persistent memory and add a normvertex having a norm vertex identifier “7” and edge directing from thenorm vertex “5” to the norm vertex “7.” Some embodiments may reserializethe deserialized directed graph to determine the updated serializedarray “[1 3 5 7]” having an associated serialized array of edges“[[1,3], [3,5], [5,7]].” The deserialization and reserialization ofdirected graph data may result in increased storage memory useefficiency, or network performance efficiency (e.g., in the case of thesmart contract being implemented on a distributed computing platform).However, while the above suggests some embodiments may implement adeserialization/reserialization operation, such operations are notnecessary. Some embodiments may forego such operations and use othermethods to benefit from increased computational performanceefficiencies, network performance efficiencies, or the like.

In some embodiments, the process 2100 may include updating a directedgraph of a smart contract program with an additional set of normvertices, as indicated by block 2148. Some embodiments may associate anadditional set of norm vertices with a directed graph of the smartcontract program in response to an amendment request causing thecreation of the additional set of norm vertices. For example, someembodiments may determine that an amendment request includesinstructions or values that causes the generation of a new norm vertexin the directed graph based on a determination that a set of conditionalstatements parameters in the amendment request is unrelated to anexisting norm vertex of the directed graph. Some embodiments may thengenerate a norm vertex by creating a new conditional statement andassociating the new conditional statement with a newly created normvertex having associated directed graph edge. In some embodiments, theamendment request may cause the creation of a plurality of norm verticesand their corresponding directed graph edges.

In some embodiments, the generated set of norm vertices may beserialized into a serialized array in a persistent memory. In someembodiments, the generated set of norm vertices may be deserialized intoa deserialized directed graph stored on a non-persistent memory, wherethe deserialized directed graph may include an adjacency matrix oradjacency list. Some embodiments may generate an edge that connects oneor more of the set of norm vertices with one or more norm vertices ofthe set of target norm vertices determined above. In some embodiments,one or more of the set of generated norm vertices may have an associatedpriority category value used to determine a sequence by which differentnorm vertices are triggered. In some embodiments, the implementation ofan execution sequence based on a set of associated priority categoryvalues may be used to reduce the risk of contradictions or logicalerrors.

Some embodiments may associate a target norm vertex with another portionof an existing directed graph based on an amendment request. The otherportion of the existing directed graph may be part of the smart contractprogram state. Alternatively, the other portion of the existing directedgraph may be part of a different smart contract program state. Forexample, an amendment request for a first smart contract program mayinclude a program identifier of a second smart contract program and avertex identifier of a first norm vertex of the second smart contractprogram. Some embodiments may associate a target norm vertex with thefirst norm vertex based on the program identifier and the vertexidentifier.

Various formats may be used to indicate this cross-program relationshipor the order by which active norm vertices are triggered acrossdifferent smart contract programs. For example, some embodiments may adda new directed graph edge to a set of directed graph edges, where thenew directed graph edge may point from the target norm vertex to thenorm vertex of the other smart contract program. Some embodiments mayaccount for potential confusion by having the directed graph vertexpoint to a dummy norm vertex, where the dummy norm vertex includesvalues identifying the norm vertex of the other smart contract program.Some embodiments may assign a priority category value to each respectivenorm vertex or respective smart contract program and refer to therespective priority category values to determine an order by which normvertices are triggered in response to an event that triggers multiplenorm vertices.

In some embodiments, the process 2100 may include storing the smartcontract program state or other symbolic AI program state in apersistent memory, as indicated by block 2152. After updating theprogram state of a smart contract program or other symbolic A model,some embodiments may then store the smart contract program in apersistent storage memory of one or more computing devices. In addition,some embodiments may store the amendment request, rejected amendmentrequests, or other data related to an amendment request in a samestorage memory.

Interactions between a pair of entities in a network of smart contractprograms often rely on the ability of the first of the pair to predictthe behavior of the second of the pair. Some entities may be able to useentity scores calculated from arithmetic operations to make suchpredictions, such as entity scores determined as a ratio of satisfiedobligations to failed obligations. Such entity scores may beinsufficient to predict entity behavior. In some cases, the normvertices and program states of a smart contract program may cause anentity to behave in ways that would not be easily captured using entityscores that do not consider specific past behaviors or environmentalchanges. Various behaviors and program state features may be used toindicate possible patterns in a graph structure. However, the number ofthe possible permutations of a directed graph and its associated programstate (which may be more than a thousand, more than a million, or morethan a billion) may prevent a significant portion of possible featuresfrom being used to determine an entity score. Furthermore, direct use ofentity scores may be infeasible in environments where an entityparticipating in a smart contract program may only be willing toparticipate if they are able to obfuscate certain values or parametersfrom other participants of the smart contract program or other possibleobservers.

Some embodiments may determine an outcome score associated with abehavior representable by a directed graph of a smart contract program(or other symbolic AI model) based on another behavior indicated by agraph portion of the directed graph or a program state of the smartcontract program. These outcome scores may be used to predict how anentity may behave in, such as whether the entity is likely to accept aset of conditional statements, demand additional conditional statements,satisfy a set of conditional statements, or fail the set of conditionalstatements. These outcome scores may also be used to characterize anentity and determine one or more entity scores for the entity, where anentity score may be equal to an outcome score or be otherwise based onthe outcome score. Some embodiments may use the outcome score or entityscore to determine which rights norms the entity may exercise orprohibition norms the entity may violate.

Some embodiments may obtain the directed graph of the smart contractprogram. Some embodiments may determine whether the directed graphincludes a graph portion that matches a graph portion template. Someembodiments may determine that a match occurs between a graph portionand a graph portion template by comparing the category labels of thenorm vertices of the graph portion and the category labels of the normvertex templates of the graph portion template. Determining a match mayalso include a comparison between other values of the graph portion andthe graph portion template, such as a condition satisfaction state, atransaction score, a set of participating entities, or the like. Anoutcome score of the first entity associated with an outcome programstate or an outcome vertex of the first entity may be determined basedon a determination of a match between a graph portion of a directedgraph and a graph portion template, where the graph portion template maybe associated with the first entity.

In some embodiments, the graph portion template may be stored in orassociated with an entity profile of the first entity. Alternatively, orin addition, the graph portion template may be stored in a generallibrary of graph portion templates and used to determine a set ofoutcome scores for each entity in a set of smart contract programs ortype of entity in the set of smart contract programs (e.g., used todetermine outcome scores for all entities having a particular role). Byusing matching graph portions with stored graph portion templates, someembodiments may provide greater context-dependent predictions whendetermining outcome scores associated with a decision or program stateof an entity and their associated entity score(s) for the entity.

In some embodiments, each respective entity vertex of an entity graphmay include or otherwise be associated with a respective entity profile.In some embodiments, the entity graph and entity profiles may be storedin the same data structure. For example, each entity profile may be usedas an entity vertex of an entity graph or otherwise be associated withthe entity vertex of the entity graph.

FIG. 22 depicts a diagram of an entity graph, in accordance with someembodiments of the present techniques. An entity graph 2200 may includea set of vertices (“entity vertices”) and edges associating the entityvertices, where the entity vertices may include or otherwise beassociated with a first entity profile 2210, a second entity profile2220, a third entity profile 2240, and a fourth entity profile 2250. Insome embodiments, each respective entity vertex of the entity graph 2200may include or otherwise be associated with a respective entity profile.In some embodiments, the entity graph and entity profiles may be storedin the same data structure. For example, each entity profile may be usedas an entity vertex of an entity graph or otherwise be associated withthe entity vertex of the entity graph. The entity profiles of the entitygraph 2200 may be stored in a storage memory of a computing device. Forexample, the entity profiles of the entity graph 2200 may be stored asobjects, lists, trees, or the like. While FIG. 2 depicts the entitygraph as comprising entity profiles directly, some embodiments mayinstead generate or otherwise update an entity graph comprising entityvertices that include values other than those included in an entityprofile or include a reference to the entity profile. In someembodiments, an entity graph may be used to determine relationships,events, types of transactions, transaction scores, or the like betweendifferent entities.

The box 2212 includes a dictionary encoding a graph portion templatethat can be visualized in a form represented in the dashed box 2270. Asshown in the box 2212, a dictionary encoding the graph portion templatemay include a first key “name” with a corresponding value “subgraph1” toindicate that the graph portion template has a name “subgraph1.” Thedictionary encoding the graph portion template in box 2212 may alsoinclude a second key “vertex_types” and a corresponding value equal to asubdictionary, where each key of the sub dictionary may include a numberand each value of the subdictionary may include a vertex property list.As shown in the box 2212, the vertex property list corresponding to thenorm vertex template having the index value of “0” may be equal to [“O”,“Failed”]. The first value of the vertex property list may indicate thecategory label “O” (which may represent an “obligation” category)associated with the first norm vertex template and the second value ofthe property list may indicate a state of the first norm vertextemplate.

In some embodiments, a category label of a norm vertex template may besimilar to or the same as those assigned to one or more norm vertices,such as “obligation,” “right,” or “prohibition.” Furthermore, someembodiments may use other characters to represent a category label. Forexample, The letters “O,” “R,” and “P” shown in FIG. 22 and used as partof the vertex property list shown in the box 2212, the box 2222, the box2224, or the box 2242 may be associated with category labels such as“obligation,” “rights,” and “prohibitions,” respectively. In someembodiments, the set of category labels may include a set of mutuallyexclusive category labels. For example, if the set of mutually exclusivecategory labels includes the category labels “obligation,” “right,” and“prohibition,” a norm vertex template categorized as being an“obligation” norm may not be categorized as being a “prohibition” norm.As further discussed below, the use of mutually exclusive categorylabels can increase the speed and reliability of a matching operationbetween a graph portion and a graph portion template.

The dictionary shown in box 2212 may also include a third key“edge_template” and the corresponding value of the third key“edge_template” may include an array of subarrays, where each subarraymay represent an edge template from a first norm vertex template to asecond norm vertex template. An edge of a directed graph may be matchedwith an edge template if the head and tail of the edge match. In someembodiments, the order of the values in each subarray may indicate adirectionality of the edge. For example, the subarray “[0,1]” mayrepresent an edge associating the first norm vertex template representedby the key-value pair “[0: [“O”, “Failed”]]” to the second norm vertextemplate represented by the key-value pair “[1: [“R”, “Failed”]],” wherethe edge may have an edge direction from the first norm vertex templateto the second norm vertex template.

The dictionary shown in block 2012 may also include a fourth key“out_det_param,” where a corresponding value of the fourth key mayinclude a set of outcome determination parameters or values otherwiseassociated with the outcome determination parameters. As describedfurther below, some embodiments may determine an outcome determinationparameter of a graph portion template using various methods such as bycalculating a ratio of a first number to a second number, by computing aratio of weighted sums, by training a machine learning model, or thelike. In some embodiments, the outcome determination parameter of agraph portion template may be equal to correlated with a predictedlikelihood of a particular outcome occurring.

In some embodiments, the set of outcome determination parameters mayinclude a value to specify a type of outcome determination operation touse. For example, the set of outcome determination parameters of the box2212 include the list ‘[“CNN”, “Satisfy”, “x1110”].’ The first elementof the set of outcome determination parameters may be an identifier ofan outcome determination model usable by a computing system to selectthe outcome determination model. For example, the value “CNN” may causea computing system to select a convolutional neural network to determinean outcome score. The second element of the list used as a value for thefourth key “out_det_param” may indicate an outcome state associated withthe outcome score being computed by a selected model. For example, thesecond element of the list ‘[“CNN”, “Satisfy”, “x1110”]’ may include thephrase “Satisfy,” which may indicate that the outcome score represents alikelihood of the first entity to satisfy an obligation. The value“x1110” may indicate a record in a database of model parameters, wherethe record may include weights, biases, hyperparameters, or other valuesof the outcome determination model used to determine an outcome score.

FIG. 22 also includes a dashed box 2270 that encloses a visualrepresentation of a part of a directed graph. The directed graphenclosed by the dashed box 2270 may match the graph portion templateencoded in box 2212. Some embodiments may determine that the first normvertex 2271 matches the norm vertex template represented by the firstkey-value pair “0:[“O”, “Failed”].’ This determination may be based onthe “obligation” label of the first norm vertex matching the “O” (whichmay be used to represent an “obligation” norm) of the second key-valuepair and the failed state of the first norm vertex 2271 matching the“Failed” state of the first key-value pair. Similarly, some embodimentsmay determine that the second norm vertex 2273 matches the norm vertextemplate represented by the second key-value pair ‘1:[“R”, “Failed”].’This determination may be based on the rights norm label of the secondnorm vertex matching the “R” of the second key-value pair and the failedstate of second norm vertex 2273 matching the “Failed” state of thesecond key-value pair. Some embodiments may determine that the thirdnorm vertex 2275 matches the norm vertex template represented by thethird key-value pair ‘2:[“R”,].’ This determination may be based on therights norm label of the second norm vertex matching the “R” of thesecond key-value pair.

As shown in the dashed box 2270, the first norm vertex 2271 may beassociated with the second norm vertex 2273 via the directed graph edge2272, and the second norm vertex 2273 may be associated with the thirdnorm vertex 2275 via the directed graph edge 2274. A determination maybe made that a directed graph edge matches with an edge template. Thisdetermination may be based on the head vertex of the directed graph edgematching with a corresponding vertex template that the edge template isdirected away from and the tail vertex of the directed graph edgematching with a corresponding vertex template that the edge template isdirected towards.

For example, some embodiments may determine that the directed graph edge2272 may match the edge template subarray ‘[0,1]’ displayed in the box2212. This determination may be made based on the tail of the directedgraph edge 2272 being the first norm vertex 2271, which matches with thenorm vertex template represented by “0:[“O”, “Failed”],’ and may also bebased on the head of the directed graph edge 2272 being the second normvertex 2273, which matches with the norm vertex template represented by“1:[“R”, “Failed”],” where the edge template subarray [0,1] directs awayfrom the first and towards the norm vertex template represented by“1:[“R”, “Failed”].” Similarly, some embodiments may determine that thedirected graph edge 2274 may match the edge template subarray ‘[1,2]’displayed in the box 2212. This determination may be made based on thetail of the directed graph edge 2274 being the second norm vertex 2273,which matches with the norm vertex template represented by “1:[“R”,“Failed”]” and may also be based on the head of the directed graph edge2274 being the third norm vertex 2275, which matches with the normvertex template represented by “2:[“R”],” where the edge templatesubarray [0,1] directs away from the norm vertex template represented by“1:[“R”, “Failed”] and towards the norm vertex template represented by“2:[“R”].”

Some embodiments may determine that a graph portion template matcheswith a graph portion(s) of a smart contract directed graph. In response,these embodiments may further provide one or more norm vertexidentifiers corresponding to the position of the graph portion in thesmart contract program directed graph. For example, a graph portion of adirected graph that includes the first norm vertex 2271, second normvertex 2273, and third norm vertex 2275 may be matched with a graphportion template. In response, some embodiments may provide anidentifier for the first norm vertex 2271, second norm vertex 2273, orthird norm vertex 2275 in association with the graph portion template.

In some embodiments, the entity graph 2200 may include an associationbetween the first entity profile 2210 and the second entity profile2220, where the association may include an entity graph edge. Forexample, the first entity profile 2210 may be associated with the secondentity profile 2220 via a first entity graph edge 2216 and a secondentity graph edge 2217. In some embodiments, the associations betweenentities an entity graph may be treated as edges having a directionalityor may be associated with a quantitative or categorical value. Forexample, a first entity graph edge 2216 may be based on a norm vertexassociated with a conditional statement that includes an allocation ofcomputing resources from the first entity “Ent1” to the second entity“Ent2.”

In some embodiments, the entity graph 2200 may include an associationbetween the first entity profile 2210 and the second entity profile2220. For example, the first entity profile 2210 may be associated withthe second entity profile 2220 via a first entity graph edge 2216 and asecond entity graph edge 2217. In some embodiments, the associationsbetween entities an entity graph may be treated as edges having adirectionality or may be associated with a quantitative or categoricalvalue. For example, a first entity graph edge 2216 may be stored as anarray [x01553e51, x022354e88] based on a norm vertex associated with aconditional statement that includes an allocation of computing resourcesfrom the first entity “Ent1” to the second entity “Ent2.” Alternatively,or in addition, the first entity graph edge 2216 or other associationsbetween different entities or between their corresponding profiles maybe stored as pointers or reference identifiers associated with theentities themselves. Furthermore, some embodiments may store a set ofassociations such as a set of entity graph edges in a single record,data object, property, or the like. For example, some embodiments maystore the first entity graph edge 2216 and second entity graph edge 2217in the form of an entity association dictionary ‘[ent1: x01553e51, ent2:x022354e88, RAM: 50, Memory: −300]’ to indicate an association based ona set of transactions or possible transactions between the first entity‘x01553e51’ and the second entity ‘x022354e88.’ The entity associationdictionary may be based on a first conditional statement of anobligation norm that would cause the first entity allocating 50 GB ofRAM from the first entity ‘x01553e51’ to the second entity ‘x022354e88’and a second conditional statement of an obligation norm that wouldcause the first entity allocating 50 GB of RAM from the first entity‘x01553e51’ to the second entity ‘x022354e88.’

In some embodiments, the entity graph 2200 may include an associationbetween the second entity profile 2220 and the third entity profile2240. For example, the entity graph 2200 may include a second set ofassociations that include the third entity graph edge 2228, a fourthentity graph edge 2229, a fifth entity graph edge 2230, and a sixthentity graph edge 2231. In some embodiments, each of the entity graphedges in the second set of associations may have a directionality fromone of the pair of entities to the other of the pair of entities basedon a transfer of data, provided service, allocation of resources, or thelike. In some embodiments, each of the associations may be based on aset of conditional statements for which the corresponding triggeringevent(s) or outcome state(s) affect the pair of entities. For example,the third entity graph edge 2228 may be associated with the transactionscore “100 GB” that is obtained from a conditional statement encodingthe allocation of 100 GB of memory from the second entity associatedwith the second entity profile 2220 to the third entity associated withthe third entity profile 2240.

In some embodiments, the entity graph 2200 may include an associationbetween the first entity profile 2210 and a fourth entity profile 2250.The fourth entity profile 2250 of the entity graph 2200 may represent averification entity that does not have any stored graph portiontemplates. The first entity profile 2210 may be associated with thefourth entity profile 2250 via the set of associations that include anentity graph edge 2218 and an entity graph edge 2219. Similarly, thesecond entity profile 2220 may be associated with the fourth entityprofile 2250 via the set of associations that include an entity graphedge 2226 and an entity graph edge 2227. Similarly, the third entityprofile data 240 may be associated with the fourth entity profile 2250via the set of associations that include an entity graph edge 2246 andan entity graph edge 2247. In some embodiments, the entity graph edgesbetween the first, second, or third entity with a verification entitymay represent associations based on messages confirming that an actionor a value has been sent to a fourth entity associated with the fourthentity profile 2250 by one of the respective entities associated withone of the other entity profiles 2210, 2220, or 2240. For example, theentity graph edges 2219, 2227, and 2247 may be based on instructionsencoding the sending of outcome scores or other values to the fourthentity and the entity graph edges 2218, 2226, and 2246 may be based oninstructions encoding the transmission of indicators indicating whetherthe sent scores or other values satisfy a set of criteria. In someembodiments, the entity associated with the fourth entity profile 2250may provide verification that another entity had satisfied a thresholdor other criteria.

Some embodiments may generate an entity graph where each of the entityprofiles associated with the vertices of the entity graph satisfies oneor more criteria. For example, while the entity graph 2200 includes thefourth entity profile 2250, some embodiments may generate an entitygraph based on a first criterion that each entity allocates or receivesallocations of computing resources with at least one other entity. Someembodiments may use this first criterion to generate an entity graphcomprising entity vertices associated with the first entity profile,second entity profile, and third entity profile without including thefourth entity profile 2250.

Some embodiments may determine one or more paths through the entitygraph 2200 to determine a relationship between different entities. Forexample, the first entity “x01553e51” is not shown to have any directassociations with the third entity “x125574f39.” However, someembodiments may determine that the first entity “x01553e51” may beassociated with the third entity “x125574f39” based on a path from thefirst entity “x01553e51” to the third entity “x125574f39” via the firstentity graph edge 2216 and the third entity graph edge 2228. Someembodiments may determine that the first entity graph edge 2216 and thethird entity graph edge 2228 form a path based on a shared resourcetype, a shared combination of resource types, shared range boundingtransaction amounts, or the like.

FIG. 23 is a flowchart of a process to assign an outcome score based ona graph portion, in accordance with some embodiments of the presenttechniques. In some embodiments, the process 2300 may include obtaininga set of smart contract programs, each of which encodes a directedgraph, as indicated in block 2304. Each program of the set of smartcontract programs may be obtained using operations similar to or thesame as those described for block 2104 above. For example, the set ofsmart contract programs may be obtained from a centralized ordecentralized computing platform executing a plurality of smart contractprograms or storing a history of smart contract programs. In someembodiments, the number of smart contract programs may be over fivesmart contract programs, over 10 programs, over 50 programs, over 100programs, or the like. For example, some embodiments may obtain over 50smart contract programs, where each respective smart contract program ofthe plurality of smart contract programs includes data encoding arespective directed graph and encoding a respective set of entitiescapable of viewing or modifying a state of the respective smart contractprogram. In some embodiments, an obtained smart contract program maystill be in operation, where the state of the smart contract programencoding the directed graph may be changed based on received eventmessages. Alternatively, or in addition, an obtained smart contractprogram may be completed and no longer responsive to event messagesreceived by a computing platform. For example, a smart contract programmay be executed to completion, where no further conditional statementsof the smart contract program can be triggered.

In some embodiments, the process 2300 may include determining a firstgraph portion template based on the directed graphs of the set of smartcontract programs, as indicated by block 2308. A graph portion templatemay include one or more norm vertex templates, one or more edgesassociating two norm vertex templates, or the like. A norm vertextemplate may include values associated with a norm vertex such as acategory label, a satisfaction state, a conditional statement, a type ofconditional statement, or the like. For example, the graph portiontemplate may include a category label “obligation” and the satisfactionstate “failed.” As further described below, the norm vertex template maybe used to determine the presence of a norm vertex, where two normvertex templates of a graph portion template may be identical to eachother or different from each other. For example, a norm vertex that isindicated to be an obligation norm that is failed may be matched with afirst norm vertex template in response to the norm vertex templateincluding or otherwise associated with the category label “obligation”and the satisfaction state “failed.” In some embodiments, an edgetemplate associating two norm vertex templates may be directed from afirst norm vertex template to a second norm vertex template. An edgetemplate associating two norm vertex templates may include an arrayspecifying the identifiers of two norm vertex templates, a transactionamount, a range of transaction amounts, or the like.

In some embodiments, the graph portion template may be associated withone or more category labels, conditional statements, or types ofconditional statements. For example, a set of category labels may beused for a set of norm vertex templates and may include mutuallyexclusive category labels. For example, a first graph portion templatemay include a first norm vertex template associated with a firstcategory label, and an edge template may associate the first norm vertextemplate with a second norm vertex template, where the second normvertex template may be associated with a second category label. In someembodiments, a conditional statement may be used to specify a graphportion template. For example, a graph portion template may include aterminal norm vertex associated with a failure to satisfy an obligation.

In some embodiments, the graph portion template may include a first normvertex template and a second norm vertex template that are not connectedto each other in the graph portion template. For example, the first normvertex template may be associated with the category label “obligationnorm” and the second norm vertex template may be associated with thesame category label “obligation,” where the first and second normvertices may represent unrelated obligations of a first entity toallocate resources to a second entity. Alternatively, or in addition,the graph portion template may include a directed path or cycleconnecting a set of norm vertex templates. For example, a first graphportion template may include a first norm vertex template associatedwith a second norm vertex template and a second norm vertex templateassociated with a third norm vertex template. In some embodiments, thegraph portion template may include a combination of single, disconnectednorm vertex templates, directed paths through norm vertex templates,circular paths through norm vertex templates, or the like, where pathsthrough a graph portion template may be determined based on a set ofedge templates.

Some embodiments may determine the first graph portion template based ona library of graph portion templates. For example, the library of graphportions templates may include a graph portion template associated witha first array indicating logical category labels of a subgraph. Variousmethods may be used to determine the presence of a subgraph in a graph.For example, some embodiments may decompose a directed graph into a setof possible subgraphs (“induced graphs”) and determine whether asubgraph is present by comparing each induced graph of the set ofpossible subgraphs with each graph portion template of a set of graphportion templates. In some embodiments, graph portion templates thatmatch one or more induced subgraphs of a directed graph may be includedin the set of graph portion templates used to determine an outcomescore. Furthermore, as discussed further below, a graph portion templatemay be associated with a specific entity or set of entities. Forexample, a graph portion may be stored in an entity profile associatedwith a specific entity. Alternatively, a graph portion template may bestored in a default set of graph portion templates that is used todetermine outcome scores for multiple entities by default.

Some embodiments may use subgraph isomorphism algorithms to determinethe presence of a subgraph in a graph, where a subgraph may be specifiedby a subgraph template. The detection of subgraph isomorphs may includealgorithms that account for the possibility that a candidate subgraph isnot present a graph (e.g., the candidate subgraph is not necessarily aninduced subgraph of the graph) on include algorithms that assume thecandidate subgraph is present (e.g., the candidate subgraph is known tobe an induced subgraph of the graph). Some embodiments may use aplurality of subgraph isomorphism algorithms to determine the presenceof a subgraph based on the subgraph structure itself. For example, someembodiments of may use algorithms to determine the presence ofthree-node subgraphs in a graph, such as algorithms developed by Itaiand Rodeh or Eisenbrand and Grandoni, as described by in “Graph patterndetection” Dalirrooyfard (STOC 2019: Proc. 51st Annual ACM SIGACTSymposium on Theory of Computing, arXiv:1904.03741), which is hereinincorporated by reference.

In some embodiments, the process 2300 may include determining a set ofoutcome determination parameters based on the set of graph portiontemplates, as indicated by block 2312. A set of outcome determinationparameters may be used by an outcome determination model to determine anoutcome score, such as a statistical prediction model, an unsupervisedlearning model, a supervised learning model, some combination thereof,or the like. As further discussed below, the outcome determination modelusing the outcome determination parameters may be used associated withan outcome score and an outcome. An outcome may include an outcomeprogram state, the occurrence of an event, the satisfaction or failureof a payment obligation by an entity, the matching of a graph portionwith a specified graph portion template, or the like.

In some embodiments, an outcome determination parameter may bedetermined using a ratio or a statistical calculation. In someembodiments, an outcome determination parameter may indicate orotherwise be correlated with a number of times that the behaviorindicated by the first graph portion template had occurred. For example,the outcome score may be a ratio of the number of times that the firstgraph portion template matches with a graph portion in the set ofdirected graphs to the number of times that the first entity hadinteracted with another entity based on a plurality of the directedgraphs of a plurality of smart contract programs. In some embodiments,an outcome determination model may be caused to use a specified set ofoutcome determination parameters for a directed graph based on thedirected graph having a graph portion matching a second graph portiontemplate, as discussed further below.

In some embodiments, the outcome determination parameters may include aset of weights, biases, or other parameters of a neural network model oranother machine-learning model. Some embodiments may determine a set ofoutcome determination parameters for a first entity by training theneural network model or another machine-learning model to use aplurality directed graphs of a plurality of smart contract programsassociated with the first entity. For example, the set of outcomedetermination parameters may include a set of neural network weights ofone or more neurons of a convolutional neural network or other neuralnetwork, The convolutional neural network or other neural network may beused to determine a likelihood of an outcome action occurring, where theoutcome action may include a transition to an outcome program state, anevent occurring, or the like. In some embodiments, the neural networkmay be trained to use the match between a graph portion and a graphportion template as a feature. For example, some embodiments may betrained using a feature that is set to “1” when a directed graph has agraph portion that matches a first graph portion template and is set to“0” when the graph portion does not match the first graph portion.Additionally, or alternatively, the feature set may include otherattributes such as global parameter values of the smart contract programstate, variables stored in a storage memory accessible to the smartcontract program, or the like.

In some embodiments, the feature set used to train a neural networkmodel or other machine-learning model to determine outcome determinationparameters based on a directed graph may include embeddings based on thedirected graph. Some embodiments may encode the entity vertices of anentity graph to determine a feature set, such as by applying anembedding algorithm such as a one-hot encoding algorithm to a set ofentity vertices. In some embodiments, the embedding assigned to arespective vertex of the directed graph may include or otherwise beassociated with a relationship between the respective entity and otherentities of the entity graph. For example, some embodiments may generatea set of entity graph features using a vertex embedding algorithm suchas using a random walk algorithm, a neural network-based embeddingalgorithm, or the like. For example, some embodiments may generate a setof embeddings for the vertices of a directed graph using a random walkalgorithm such as a DeepWalk algorithm, a Node2Vec algorithm, or thelike. Use of a random walk algorithm may include performing random walksampling for each vertex of the directed graph, training a skip-grammodel by using the random walks as one-hot vectors and computing anembedding based on an output of the trained skip-gram model.

Alternatively, or in addition, some embodiments may generate the set ofentity graph features using a neural network-based algorithm such as astructural deep network embedding (SDNE) algorithm. For example, someembodiments may use a set of autoencoders that may take a node adjacencyvector as an input and is trained to reconstruct the node adjacencyvector based on a second-order adjacency. The adjacency vector of avertex may be represented as a vector where non-zero elements representa connection between the vertex and other vertices a graph. Afirst-order adjacency vector for a first vertex may represent theadjacency vector for the first vertex itself, and a second-orderadjacency vector of the first vector may represent the adjacency vectorfor a neighboring vertex of the first vertex. Some embodiments may usethe adjacency vectors of a directed graph vertex as a set of embeddingsfor the directed graph or otherwise determine the set of embeddingsbased on the adjacency vector outputs of the set of autoencoders. Whilethe above describes the use of random walk algorithms or neuralnetwork-based algorithms to generate a set of embeddings, various otheralgorithms or methods may be used to determine embeddings for a graph.For example, some embodiments may use a graph factorization embeddingalgorithm, GraRep embedding algorithm, locally linear embeddingalgorithm, laplacian eigenmaps embedding algorithm, high-order proximitypreserved embedding algorithm, deep network embedding for graphrepresentation embedding algorithm, graph convolutional neural networkembedding algorithm, graph2vec algorithm, or the like.

In some embodiments, a set of features used to train an outcomedetermination model for a first entity may include data encoding orotherwise representing statuses of other entities of an entity graphthat includes the first entity. For example, a feature used by anoutcome determination model for a first entity may include or be basedon a feature matrix representing whether other entities of the entitygraph have been failed by the first entity. Alternatively, or inaddition, a feature may include an entity graph feature that indicates aconnectedness of the graph to a specific entity, a global parameter notspecific to an entity (e.g., an index stock price, a system temperaturevalue, or the like), or the like. For example, some embodiments may usethe graph2vec algorithm, which may include sampling a set of entitysubgraphs in an entity graph, training a skip-gram model based on theset of entity subgraphs, and determining entity embeddings based on thetrained skip-gram model. Some embodiments may then train a neuralnetwork model using the set of entity embeddings to determine an outcomedetermination parameter.

In some embodiments, a feature used for training or using a machinelearning model may include or be based on a set of transaction amountsindicating an amount that has been transferred to or from a respectiveentity associated with respective entity vertex with respect to each ofthe other entities that have had a transaction with the respectiveentity. Alternatively, or in addition, the feature may include or bebased on a count of transactions that had occurred between therespective entity and other entities of an entity graph. For example, afeature for training or using a machine-learning model associated with afirst entity may include a ratio of the number of times that the firstentity had failed an obligation norm when dealing with a second entityto a number of times that the first entity had a transaction with thesecond entity. Alternatively, or in addition, this ratio or anothervalue based on a count of transactions may be used as an outcomedetermination parameter.

In some embodiments, the process 2300 may include obtaining a firstdirected graph of smart contract program of a first entity, as indicatedby block 2320. Operations to obtain the smart contract program of thefirst entity may be similar to or the same as those described for block2104 above.

In some embodiments, the process 2300 may include obtaining a set ofgraph portion templates based on the first entity, as indicated by block2324. Some embodiments may obtain the graph portion templates byobtaining an entity profile of an entity, where the entity profileincludes or is otherwise associated with graph portion templates. Asdiscussed above, the set of graph portion templates may be determinedbased on a set of smart contract programs associated with the firstentity. In some embodiments, an entity profile may include an entityidentifier, a set of values indicating one or more attributes associatedwith an entity, a set of entity scores, or the like. For example, anentity profile may include a set of display names, set of internalalphanumeric identifiers of the entity, set of numeric values orcategories indicating behaviors, a set of numeric values or categoriesindicating limits or numeric ranges, or the like. Some embodiments maystore a set of graph portion templates in an entity profile or otherwiseassociate the graph portion template with the entity profile. Forexample, some embodiments may store the display name “entity 553,” theentity role name “resource allocator,” a maximum resource allocationlimit “106 GB,” and a set of graph portion templates in the first entityprofile. In some embodiments, a single entity may be associated withmore than one entity profile. Alternatively, or in addition, the set ofgraph portion templates may be obtained from a library of graph portiontemplates that is either not associated with an entity profile or isassociated with multiple entity profiles. For example, some embodimentsmay obtain a default set of graph portion templates to determine anoutcome score for a first entity from a library of graph portiontemplates that is not stored in an entity profile of the first entity.

In some embodiments, the process 2300 may include determining a set ofoutcome scores based on the first directed graph of the first entity,the set of outcome determination parameters, or the set of graph portiontemplates, as indicated by block 2332. As described above, someembodiments may load or otherwise obtain a set of outcome determinationparameters and use the obtained outcome determination parameters todetermine an outcome score using an outcome determination model. Theselection of an outcome determination model and its corresponding set ofoutcome determination parameters may be based on values stored in anentity profile of a first entity or otherwise based on values associatedwith the first entity. For example, a first entity profile may include aplurality of sets of outcome determination parameters, where each set ofthe plurality of sets causes the use of a respective outcomedetermination model and a respective set of outcome determinationparameters for the respective outcome determination model.

The outcome determination model may include a model for determiningpredictions or the likelihood of an outcome such as a statistical model,a machine learning model, or the like. The outcome may include variouspossible outcomes, such as the satisfaction or failure of an obligationby an entity, the activation of a vertex having a specified categorylabel, or the like. For example, some embodiments may determine that afirst set of outcome determination parameters stored in an entityprofile associated with a first entity specifies the use of aconvolutional neural network as an outcome determination model. Someembodiments may then use a weights array identifier stored in the set ofoutcome determination parameters to obtain a set of weights and biasesfrom a database of parameters. Some embodiments may then use the set ofweights and biases for the convolutional neural network to determine anoutcome score, where another value stored in the set of outcomedetermination parameters may indicate that the outcome score includes ameasurement of the likelihood that the first entity will fail anobligation.

As described above, determining the outcome score may includedetermining whether a graph portion of the first directed graph matchesa graph portion template of the set of graph portion templates. Someembodiments may use one or more graph isomorphism algorithms todetermine whether a graph portion matches a template of the set of graphportion templates. In response to a determination that a match exists,some embodiments may update an input value of the outcome determinationmodel, where the input value indicates that the graph portion matches agraph portion template. Some embodiments may then determine an outcomescore based on the input value. For example, some embodiments may obtaina count of the number of times that the graph portion template matches agraph portion of the directed graph and use the count as an input valuefor a machine learning model to determine an outcome score. In someembodiments, the set of graph portion templates stored in the entityprofile of a first entity or otherwise associated with the first entitymay include a norm vertex template, an edge between norm vertextemplates, a path through a plurality of vertex templates, or the like.Furthermore, in some embodiments, the outcome score may be one of a setof outcome scores, where each score of the set of outcome scores isdetermined using one of a set of outcome determination parameters.

In some embodiments, the process 2300 may include determining whetherthe outcome score satisfies an outcome score threshold, as indicated byblock 2340. In some embodiments, the outcome score threshold may includea predetermined value. For example, an outcome score may indicate thelikelihood that the first entity participates in a smart contract thatincludes a directed graph having a specified graph portion that matchesa target graph portion template. For example, the outcome scorethreshold may be equal to 50%, and satisfying the outcome scorethreshold may indicate that at least 50% of the smart contracts in whichthe first entity participates include a directed graph having a graphportion that matches a target graph portion template. In someembodiments, each entity may include a plurality of outcome scores,where each of the plurality of outcome scores may have a correspondingoutcome score threshold. If the outcome score satisfies the outcomescore threshold, operations of the process 2300 may proceed tooperations described for block 2344. Otherwise, operations of theprocess 2300 may proceed to operations described for block 2348.

In some embodiments, the process 2300 may include storing a valueindicating that the outcome score threshold is satisfied, as indicatedby block 2344. The value indicating that the outcome score threshold issatisfied may be stored in various forms. For example, the valueindicating outcome score threshold satisfaction may include a booleanvalue, a number representing that the outcome score threshold issatisfied, an alphanumeric string such as “satisfied,” a value of adictionary or object property, or the like. As discussed further below,the value indicating outcome score threshold satisfaction may be storedin an entity profile, a database of values, or some other data structurestored on persistent computer memory. In some embodiments, the value maybe stored on a tamper-evident, distributed ledger operating on adistributed computing platform. Alternatively, or in addition, the valuemay be stored on a centralized computing platform, such as on a singlecomputing device. Furthermore, as described elsewhere in thisdisclosure, updating the value indicating outcome score thresholdsatisfaction may include updating an entity graph.

In some embodiments, the process 2300 may include updating an entityscore based on the outcome score, as indicated by block 2348. Variousmethods may be used to determine an entity score based on the outcomescore. For example, some embodiments may set an entity score to be equalto an outcome score. Alternatively, or in addition, an entity score maybe equal to a weighted sum of entity scores. For example, someembodiments may determine an entity score to be equal to a set ofoutcome scores equal to a set of probabilities of obligation normfailure multiplied by a corresponding transaction score.

In some embodiments, updating the entity score based on the outcomescore may include updating an entity profile associated with the entitygraph, where the entity profile includes or is otherwise associated withthe entity score. In some embodiments, the values updating an entityprofile of the first entity may then also be used to update additionalentity profiles of other entities of an entity graph. Some embodimentsmay store entity profile values or entity graph values on atamper-evident, distributed ledger operating on a distributed computingplatform. Alternatively, or in addition, the value may be stored on acentralized computing platform, such as on a single computing device.

Furthermore, in some embodiments, the entity score may be used in a setof simulations to determine whether an entity is likely to accept orreject amendments to the conditional statements of a smart contractprogram or the structure of a directed graph of the smart contractprogram. For example, a first outcome score may represent a likelihoodof a first entity to fail an obligation based on a simulation of a firstsmart contract program and a second outcome score may represent alikelihood of the first entity to fail an obligation based on asimulation of a second smart contract program. Based on a comparison ofthe first outcome score and the second outcome score, some embodimentsmay determine the likelihood that a second entity is likely to accept orreject a proposed amendment to a conditional statement used in the firstand second smart contract programs.

FIG. 24 is a flowchart of a process to send a message indicating that anentity score has been updated based on an entity graph, in accordancewith some embodiments of the present techniques. In some embodiments,operations of the process 2400 may include updating an entity graphbased on an outcome score associated with a first entity or a secondentity of the entity graph, as indicated by block 2404. In someembodiments, updating an entity graph may include updating an entityscore of the entity graph based on an outcome score determined using oneor more of the operations described above for the process 2300. Forexample, an entity score for a first entity may be equal to an outcomescore described above. Alternatively, or in addition, an entity scoremay be based on a combination of outcome scores. For example, in someembodiments, an entity score may be equal to a weighted sum of a firstoutcome score based on the number of times the entity fails two or moreobligation norms and a second outcome score based on the number of timesthe entity triggers a rights norm to prematurely terminate a smartcontract program.

In some embodiments, the entity score, entity profile associated withthe entity score, or entity graph associated with the entity score maybe stored on a distributed, tamper-evident ledger of a distributedcomputing platform. For example, a version of a first entity profile, asecond entity profile, and an entity graph edge associating the firstentity profile to the second entity profile may be stored on thedistributed, tamper-evident ledger (e.g., stored as on-chain data).Alternatively, or in addition, some or all of the entity graph may bestored on storage memory that is not part of a distributed,tamper-evident ledger. For example, some embodiments may store datarelated to the entity graph in a storage memory of a centralizedcomputing platform, where the data may be accessible and transferredover a distributed computing platform via a verification hash value. Forexample, some embodiments may update data related to a first entityprofile by transmitting an updated score from a distributed computingplatform to the centralized computing platform. Receiving the updatedscore may cause the centralized computing platform to update the firstentity profile based on the updated score. In some embodiments, theupdated score is not stored in a persistent memory of the distributedcomputing platform and is stored in a persistent memory of thecentralized computing system, where the updates score may be obtainedfrom the centralized computing system using data provided from thedistributed computing platform.

Some embodiments may detect the similarity between two different entityprofiles based on a set of entity similarity criteria. For example, someembodiments may compare a first entity profile and a second entityprofile based on a set of predetermined fields such as an entity name,an account number, an identifier value, entity role, a hashed loginvalue, or the like. Some embodiments may determine a similarity valuebased on a ratio of identical values in the set of predetermined fields.For example, some embodiments may determine that two entity profilesshare a same entity name, a same entity role, and a same accessprivilege and, in response, determine that the two entity profilessatisfy an entity similarity criterion. In some embodiments, thedetermination that two entities satisfy a set of entity similaritycriteria may cause a smart contract program or other symbolic AI programto generate a message indicating the two entity profiles. By indicatingsimilarities between two entity profiles, some embodiments may reducethe risk of hacking attempts, counterfeiting attempts, unnecessaryduplication of roles, or the like.

In some embodiments, operations of the process 2400 may includedetermining whether the first entity failed or satisfied a conditionalstatement associated with the second entity, as indicated by block 2410.In some embodiments, an event message may be obtained by a smartcontract program that triggers a norm vertex associated with the firstentity, where the event message causes the first entity to satisfy orfail a conditional statement associated with the norm vertex. Forexample, a conditional statement may correspond to an obligation normvertex in a set of norm vertices, where a condition of the conditionalstatement may include the condition that the first entity sends anaccount renewal message to the second entity. In response to the firstentity sending the account renewal message to the second entity, someembodiments may determine that the first entity has satisfied theobligation norm vertex. Alternatively, or in addition, an event messagemay be provided by the second entity, a third-party entity, anotherapplication operating on a computing platform executing the smartcontract program or other symbolic AI program, an API of the smartcontract program, or the like. For example, a conditional statement maycorrespond to an obligation norm vertex in a set of norm vertices, wherethe conditional statement may include a condition that the second entitysends a confirmation message indicating that it is able to use computingresource allocated by the first entity or has received a correspondingamount of a digital asset equivalent to the computing resource from thefirst entity. In response to the second entity not sending theconfirmation message, some embodiments may determine that the firstentity has failed a conditional statement associated with the secondentity.

In some embodiments, operations of the process 2400 may include updatingan association between a first entity profile and a second entityprofile, as indicated by block 2414. The association between the firstentity profile the second entity profile may be a part of the entitygraph and may be stored as an entity graph edge associating a firstentity vertex corresponding with the first entity profile and a secondentity vertex corresponding with the second entity profile. Theassociation between the first entity profile and the second entityprofile may be stored in various forms associating a plurality of entityprofiles, such as an array, a pointer from one of the entity profiles toanother of the entity profiles, an identifier stored in one of theentity profiles that may be used to find another of the entity profile,or the like.

In some embodiments, updating the association between the first entityprofile the second entity profile may be based on a transaction scorebetween the first entity profile and the second profile. The transactionscore may be used as a part of a conditional statement of a norm vertexor may otherwise be associated with the norm vertex. For example, aconditional statement of a rights norm vertex may indicate that a firstentity of a set of norm vertices may trigger a rights norm vertex andcause a second entity to transfer 100 units of a digital asset to thefirst entity, where a transaction score of the rights norm vertex may beor otherwise include the 100 units. In response, some embodiments mayupdate the association between the first entity profile the secondentity profile by increasing an association value by 100 units. In someembodiments, the association between entity profiles may be updated evenif there is no indication that an entity has satisfied or failed aconditional statement. For example, some embodiments may perform regularupdates of an entity graph to update entity graph edges between entityvertices on an entity graph based on parameters of the conditionalstatements of a set of symbolic A programs, where each entity vertexcorresponds with an entity profile.

In some embodiments, an entity graph may include a plurality of entitygraph edges between a first entity profile and a second entity profileof the entity graph. For example, each entity graph edge of theplurality of entity graph edges may represent a different resource typebeing used in a transaction between the different entities. Someembodiments may update an entity graph edge of the entity graph based ona resource type associated with a transaction score. For example, afirst entity profile may be associated with a second entity profile viaa first entity graph edge and a second entity graph edge, where thefirst entity graph edge represents a computing resource allocation andthe second entity graph edge represents a time allocation. In responseto determining that an event message indicates that the first entity hasallocated 500 GB of storage memory to the second entity, someembodiments may update the entity graph by updating a score associatedwith the first entity graph edge and not updating a score associatedwith the second entity graph edge. Some embodiments may update thenetwork by updating the association representing storage memoryallocation between the first entity and the second entity by adding 500GB to the storage memory.

In some embodiments, the process 2400 may include updating a firstentity score of the entity graph based on the satisfaction or failure ofthe conditional statement, as indicated by the block 2418. As describedabove, an entity score for an entity may represent one of various typesof attributes or behaviors of the entity and may include attributes orbehaviors associated with the satisfaction or failure of norm verticesof a smart contract directed graph. For example, some embodiments mayupdate an entity score of the first entity that represents the number oftimes that the first entity has failed an obligation. Some embodimentsmay include a plurality of entity scores associated with the entityprofile. For example, some embodiments may include an entity profilestoring or otherwise associated with a first entity score and a secondentity score, where the first entity score may indicate a number oftimes that the first entity has failed an obligation norm and a secondentity score may indicate a total amount of times that the first entityhas exercised a rights norm resulting in another entity transferring anamount of digital assets to the first entity.

Some embodiments may obtain a previous entity score stored on adistributed, tamper-evident ledger and update the entity score based onthe previous entity score. For example, some embodiments may obtain aprevious entity score of a first entity equal to a ratio representingthe number of satisfied obligations to the number of total obligations“975/1000.” In response to an event message indicating that the firstentity has satisfied another obligation, some embodiments may update theentity score by adding one to the numerator and denominator of theprevious entity score to result in the ratio “976/1001.”

In some embodiments, the first entity score may be stored in the firstentity profile or otherwise associated with the first entity profile.For example, the first entity score may part of an entity profile storedon a tamper-proof, distributed ledger. In some embodiments, a firstentity score may be encrypted and updating the first entity score mayinclude sending an encryption key in conjunction with or otherwiseassociated with update values, where the update values may be used toupdate the first entity score or otherwise update the first entityprofile. Some embodiments may obtain access to the encryption key or useof the encryption to a specified set of entities. For example, someembodiments may provide the encryption key associated with the firstentity to the first entity itself, entities associated with one or moretransactions with the first entity, a verified set of third-partyentities, or the like. By securing the first entity score based on anencryption key, some embodiments may reduce the risk of unauthorized orotherwise inappropriate changes to an entity score of the first entityprofile.

In some embodiments, operations of the process 2400 may includedetermining whether the entity score satisfies an entity scorethreshold, as indicated by block 2430. In some embodiments, the entityscore threshold may be associated with a predetermined value or becontrollable by a verification entity. Some embodiments may include aplurality of entity score thresholds, where each of the entity scorethresholds may be associated with a specific type of entity score. Forexample, some embodiments may include a first entity score thresholdrepresenting a threshold amount of computing resources allocated over aperiod of time and a second entity score threshold representing athreshold amount of energy consumed by the first entity. If the entityscore satisfies the entity score threshold, operations of the process2400 may proceed to operations described for block 2434. Otherwise,operations of the process 2400 may proceed to operations described forblock 2440.

In some embodiments, operations of the process 2400 may include storinga set of satisfaction values indicating that the first entity satisfiesthe entity score threshold, as indicated by block 2434. In someembodiments, the satisfaction value may be determined and shared withthe first entity profile by a verification entity. For example, averification entity may determine that a first entity satisfies entityscore threshold, indicating that the first entity fulfills a requiredpercentage of obligation norms. In response to this determination, someembodiments may store a satisfaction value in the entity profile of thefirst entity, where the satisfaction value indicates that theverification entity has determined that the entity score of the firstentity satisfies the entity score threshold. Alternatively, or inaddition, the satisfaction value indicating that the first entitysatisfies the entity score threshold may be stored in a storage memoryof the verification entity or in another data storage system.

By storing the satisfaction value indicating that the first entitysatisfies the entity score threshold, other entities may then be allowedto determine that the first entity satisfies the entity score thresholdwithout requiring additional computations that may slow down ondistributed computing platforms. Furthermore, a verification entity mayprovide a satisfaction value indicating that a first entity satisfiesthe entity score threshold. Providing satisfaction values from averification entity may enable some embodiments to protect the privacyof a first entity while still allowing other entities to trust orotherwise predict the behavior of the first entity.

In some embodiments, operations of the process 2400 may includedetermining whether a score access passkey value is obtained, asindicated by block 2440. The score access passkey value may be obtainedby a smart contract management system to determine whether an entityscore associated with an entity profile can be shared with ascore-requesting entity. In some embodiments, the score access passkeyvalue may be obtained as a part of another message sent to an API. Insome embodiments, the score access passkey may include a series ofalphanumeric characters, a set of values, hashed value(s), or the like.In some embodiments, the score access passkey for a satisfaction valueof a first entity may be received from the first entity itself.Alternatively, or in addition, the score access passkey may be obtainedfrom a second entity associated with the first entity via a transactiongraph or an authenticated third party. If a determination is made thatscore access passkey is received, operations of the process 2400 mayproceed to block 2444. Otherwise, operations of the process 2400 may beconsidered as complete.

In some embodiments, operations of the process 2400 may include sendinga satisfaction value to the third party, as indicated by block 2444. Insome embodiments, the satisfaction value may store or indicate a set ofvalues indicating that the first entity satisfies the entity scorethreshold. Some embodiments may send a message to another entity or anAPI based on the value indicating that the entity score satisfies theentity threshold. For example, some embodiments may send a messageindicating that a first entity score of a first entity satisfies theentity threshold to an application program interface of a second entity.Furthermore, as discussed above, some embodiments may determineadditional entities that may be associated with the first entity basedon a path through an entity graph and send the message to theseadditional entities as well. Furthermore, some embodiments may provide asatisfaction value without any score access passkey being obtained. Forexample, some embodiments may provide a first satisfaction value to setof entities without obtaining a score access passkey associated with thefirst satisfaction value but require that a score access passkey bereceived from an entity before providing the entity with a secondsatisfaction value.

The use of applications executing on a decentralized computing platformor decentralized, tamper-evident data stores in real-world situationsposes technical challenges in areas such as information retrieval speed,analytical capabilities, and data privacy. Such problems may includememory storage issues, speed bottlenecks on data transfer, data updateoperations, or difficulties in processing complex operations in adistributed computing environment. For example, a query message encodinga graph portion template may require a non-serialized directed graph tomatch a graph portion with the graph portion template or to satisfy thequery otherwise. The deserialization and reserialization operations maycause time delays that last longer than minutes or tens of minutes orcause computing devices to perform redundant computations. Such delaysmay make real-world operations unmanageable when multiple queries arebeing concurrently executed in a distributed computing environment. Inaddition, multi-participant applications executing on a decentralizedcomputing platform may include access permission mechanisms on the typeof information accessible to different types of entities. Such accesspermission mechanisms may often exacerbate the difficulty querying anapplication executing on a decentralized computing platform.

Some embodiments may use a hybrid computing system capable ofconcurrently and independently executing operations that change programstate for an application executing on a decentralized computing platformand executing query logic to retrieve or update data associated withprogram state for the application. The hybrid computing system mayinclude a set of computing devices acting as nodes of a decentralizedcomputing platform capable of executing an application based on eventscaused by a set of participating entities. The hybrid computing systemmay also store program state encoding a directed graph on adecentralized, tamper-evident data store. Some embodiments may includeoperations to serialize the directed graph for storage in a persistentstorage of a set of computing devices acting as nodes of a network usedto operate a decentralized computing platform.

In addition, the hybrid computing system may include a second data storeaccessible to one or more computing devices of the network of computingdevices. As discussed further below, the second data store may include alocal persistent storage of a computing device, a persistent storage ofa cloud computing server, a persistent storage of a seconddecentralized, tamper-evident data store, or the like. In someembodiments, the use of the second data store may increase efficiencywhen executing query logic to retrieve, analyze, or update directedgraphs or other data associated with program state for an applicationexecuting on a decentralized computing platform. Some embodiments maydeserialize a directed graph at a second computing device to generate asecond instance of the directed graph in a non-serialized format, suchas one of the non-serialized formats of a directed graph as describedabove. Some embodiments may store the second instance of the directedgraph with associated data in a second persistent storage accessible tothe second computing device. Upon receipt of a message, some embodimentsmay retrieve, analyze, or otherwise use values stored in the secondpersistent storage to provide a response value.

In some embodiments, the message to retrieve, analyze, or update valuesassociated with a directed graph may include data specifying specificgraph portions such as a graph portion template that includes labelsspecific to vertices of the directed graph. Some embodiments may matchgraph portions of a directed graph to the graph portion template todetermine a subset of vertices relevant to or otherwise targeted by amessage to determine the response value. As further discussed in thisdisclosure, the response value may be based on the subset of vertices,such as data encoded in the conditional statements of the subset ofvertices, events associated with a subset of vertices, a countassociated with the subset of vertices, or the like. For example, someembodiments may determine a specific change in a transaction scoreassociated with a vertex, a timestamp indicating when a vertex of thesubset of vertices was triggered, a computational result based on dataassociated with events causing the activation or the triggering of thesubset of vertices, or the like. By separating application executionlogic and query logic, a hybrid decentralized computing system mayprovide the transaction stability and trustworthiness of a blockchainsystem with the speed and analytical capabilities of systems based ondata stored in a centralized data store. While some embodiments may bedisclosed as including a specified feature or performing a specifiedoperation, some embodiments may enjoy disclosed benefits withoutincluding the specified feature or without performing the specifiedoperation.

In some embodiments, a message may include or otherwise be associatedwith an entity identifier useful for determining whether a set of dataretrieval criteria associated with the subset of vertices or theircorresponding entities is satisfied. For example, a first entity may notpermit certain entities or only permitted certain entities to retrievedata associated with the first entity. In some embodiments, a responsevalue may be modified or stopped from being sent if one or more dataretrieval criteria are not satisfied by the message. By using graphvertex-level granularity to determine which criteria of a set of dataretrieval criterion are applicable, the analytical capabilities of ahybrid computing system may be largely preserved while maintainingstrong data privacy protection for participating entities of atechnology-based self-executing protocols (e.g., a smart contract) orother symbolic AI applications.

FIG. 25 depicts a diagram of a hybrid decentralized computing systemusable for separating application execution logic from query logic, inaccordance with some embodiments of the present techniques. Thecomputing environment 2500 may include a hybrid network 2510, where thehybrid network 2510 includes a set of public peer node computing devices2512 and a set of privately-controlled peer node computing devices2514-2516. As shown in the hybrid network 2510, the first private peernode computing device 2514 may include a first persistent storage 2534and the second private peer node computing device 2515 may include asecond persistent storage 2535. In some embodiments, the firstpersistent storage 2534, second persistent storage 2535 or otherpersistent storage devices of the hybrid network 2510 may include one ormore physical components usable as persistent storage. For example, apersistent storage may include a set of solid-state drives, a set ofspinning disk hard drives, or the like.

In some embodiments, data based on program state for an applicationexecuting on a decentralized computing platform, records of a blockchain(e.g., blocks linked by cryptographic hashes), or other values may bedistributed between one or more nodes of the hybrid network 2510. Asdiscussed in this disclosure, program state for an application mayinclude a directed graph, where it should be understood that data in aprogram state need not be labeled as a graph in program state or programcode to constitute as one. Some embodiments may determine that adirected graph is present in program code if one or more characteristicsof a directed graph are present in the program code. In someembodiments, characteristics of a directed graph may include having aset of values representing vertices (e.g., numbers, characters, strings,or the like) and directions between the set of vertices. For example, agraph encoded in program state may include a set of arrays, entries in arelational database, objects in an object-oriented programming, or thelike.

In some embodiments, program state may be distributed from the publicpeer node computing device 2512 to other nodes of the hybrid network2510, such as the first private peer node computing device 2514, andstored in the first persistent storage 2534. Alternatively, or inaddition, program state may be distributed from the private peer nodecomputing device 2514 to other nodes of the hybrid network 2510, such asthe second private peer node computing device 2515 and stored in thesecond persistent storage 2535. In some embodiments, different instancesof data may be stored in a same persistent storage. For example, thesecond persistent storage 2535 may include a first instance of thedirected graph stored in a serialized data format that is distributed toother nodes of the hybrid network 2510. The second persistent storage2535 may also include a second instance of the directed graph stored ina non-serialized data format that is used for other computing operationssuch as data retrieval or data analysis.

In some embodiments, each of the set of private peer node computingdevices 2514-2516 may be controlled by or otherwise associated with adifferent entity and not controllable by other entities. For example,the first private peer node computing device 2514 may be associated witha first entity or be an on-premise server of a first organization, andthe second private peer node computing device 2515 may be associatedwith a second entity or be an on-premise server of a secondorganization. Furthermore, each device of the set of public peer nodecomputing devices 2512-2513 may be operating as nodes having differentpermission levels or roles in an implementation of a consensusalgorithm, as described further below. For example, the public peer nodecomputing device 2512 may serve as an observer and validator of blockswithout the ability to generate new blocks of a blockchain operating onthe hybrid network 2510.

In some embodiments, the set of private peer node computing devices2514-2516 may be able to perform operations that nodes of the set ofpublic peer node computing devices 2512 may not be permitted to perform.Alternatively, or in addition, the set of public peer node computingdevices 2512-2513 may be able to perform operations that nodes of theprivate peer node computing devices 2514-2516 may not be permitted toperform. Some embodiments may update program state for an instance of anapplication executing on a decentralized computing platform or otherwiseupdate data stored in a first decentralized, tamper-evident data store(e.g., a peer-to-peer data-sharing network) of the application. As usedin this disclosure, it should be understood that executing anapplication on a decentralized computing platform may include executingan instance of the application on a computing device of thedecentralized computing platform. Some embodiments may operate acrossthe hybrid network 2510 using the set of public peer node computingdevices 2512-2513 and the set of private peer node computing devices2514-2516. For example, the first decentralized, tamper-evident datastore may be used to store a blockchain, where both the set of privatepeer node computing devices 2514-2516 and the set of public peer nodecomputing devices 2512-2513 may be used to verify the updates to thefirst decentralized, tamper-evident data store. Some embodiments assigndifferent roles to different subsets of computing devices in a networkof computing devices based on various blockchain architectures, such asthat used for the Ethereum Delegated Proof of Stake (DPoS) system,Facebook Libra blockchain system, or the like.

In some embodiments, data may be provided to the hybrid network 2510 viaa middleware system 2520. The middleware system 2520 may include variousmiddleware systems or services. In some embodiments, the middlewaresystem 2520 may include a centralized oracle system, such as acustom-built subsystem that includes an API of an application executingon a decentralized computing platform to receive data at a node of anetwork. For example, in some embodiments, external data such as aprice, indicator of equipment failure, or environmental parameter changemay be provided to the hybrid network 2510 to an API of an applicationexecuting on a decentralized computing platform operating across thehybrid network 2510. The external data may be directly received andvalidated at the first private peer node computing device 2514 beforebeing distributed to other computing devices of the hybrid network 2510.In some embodiments, the middleware system 2520 may include adecentralized oracle network, such as the ChainLink network as describedby “ChainLink: A Decentralized Oracle Network” (Ellis, Steve; Juels,Ari; and Nazarov, Sergey; Published 4 Sep. 2017), which is herebyincorporated by reference. For example, some embodiments may use adecentralized oracle network to obtain data from a cloud data warehousesuch as Google BigQuery and use the data to update program state for anapplication executing on the hybrid network 2510. As further describedbelow, in some embodiments, an out-of-network computing device 2540 maysend data to the middleware system 2520 to update a system or mayreceive data from the middleware system 2520.

Alternatively, or in addition, data based on program state, such as aninstance of a directed graph indicating state for a smart contract, maybe stored in the external persistent storage 2542 of the out-of-networkcomputing device 2540. In some embodiments, the out-of-network computingdevice 2540 may include an enterprise server that is accessible to oneor more computing devices of the hybrid network 2510. For example, theexternal persistent storage 2542 may be accessible to the hybrid network2510. Alternatively, or in addition, the out-of-network computing device2540 may include a cloud server or a component of a cloud server. Forexample, out-of-network computing device 2540 may include a cloudcomputing server allocated for use as a part of a cloud computingservice such as Amazon AWS server, Microsoft Azure server, Google cloudplatform server, or the like. Alternatively, or in addition, theout-of-network computing device 2540 may be operating as a peer node ofa second decentralized, tamper-evident data store. For example, thesecond decentralized, tamper-evident data store may include a networkoperating under the Interplanetary File System (IPFS) protocol, Swarmprotocol, or the like. For example, some embodiments may store adirected graph in a non-serialized format in a persistent storage of acomputing device participating in a network operating under the IPFSprotocol. Data stored in a peer-to-peer data-sharing network may bedistributed to other peers of the data-sharing network, which mayincrease the security, verifiability, and resiliency to failure of datastored in the external persistent storage 2542. Furthermore, in someembodiments, a computing device may operate as a node of a first networkused to execute an application and may also operate as a node of asecond network used to operate the second decentralized, tamper-evidentdata store, where the first network and second network may use differentsets of computing devices.

In some embodiments, a message may be received by a computing device ofthe hybrid network 2510, the middleware system 2520, or theout-of-network computing device 2540 to obtain a response value based onan instance of a directed graph stored in a persistent storageaccessible to the hybrid network 2510. Some embodiments may select anon-serialized instance of the directed graph stored in a persistentstorage, such as the second persistent storage 2535 or the externalpersistent storage 2542, to determine the response value based on thedirected graph. For example, the message may cause some embodiments toobtain a set of timestamps associated with vertices of the directedgraph stored in the second persistent storage 2535 and use the data tocompute a response value.

FIG. 26 depicts a flowchart of a process to retrieve a response valuefrom a hybrid decentralized computing system based on a message, inaccordance with some embodiments of the present techniques. FIG. 26 is aflowchart of processes that may be implemented in the computingenvironment of FIG. 25 to provide a response value, in accordance withsome embodiments. For example, the process may execute one or moreroutines in the computing environment 2500. In some embodiments, thevarious operations of the process 2600 may be executed in a differentorder, operations may be omitted, operations may be replicated,additional operations may be included, some operations may be performedconcurrently, some operations may be performed sequentially, andmultiple instances of the process 2600 may be executed concurrently,none of which is to suggest that any other description herein is limitedto the arrangement described. In some embodiments, the operations of theprocess 2600 may be effectuated by executing program code stored in oneor more instances of a machine-readable non-transitory medium, which insome cases may include storing different subsets of the instructions ondifferent physical embodiments of the medium and executing thosedifferent subsets with different processors, an arrangement that isconsistent with use of the singular term “medium” herein.

In some embodiments, the process 2600 may include operations to updateprogram state for a symbolic AI application at a first computing deviceof a computing system, as indicated for block 2608. The first computingdevice may operate as a node of a network operating executing a smartcontract or other symbolic AI application. In some embodiments, thenetwork may communicatively couple some or all of the nodes of thenetwork to each other. For example, the first computing device mayupdate a directed graph encoded in the form of a set of data tables byappending a value to a set of values representing vertices of thedirected graph or changing the status of a vertex from “not triggerable”to “triggerable.”

In some embodiments, the directed graph may be updated in anon-persistent storage and serialized into a serialized data format as aserialized set of vertices. For example, the directed graph may includea set of vertices and a set of directed graph edges associating pairs ofthe set of vertices together by including a serialized array, such asthe array “[[1,2], [2,3], [3,4]].” As discussed in this disclosure, thedirectionality indicated by a graph edge may be used to determine whichnorm vertex is activated (i.e. set to be triggerable) after another normvertex has been triggered. For example, a directed graph edge may pointfrom a tail vertex to a head vertex. The directionality of the edge mayindicate that the conditional statement of the head vertex is set to betriggerable when the conditional statement of the tail vertex has beensatisfied or failed. (though this directionality may be arbitrary. Someembodiments may then store the serialized set of vertices in a firstpersistent storage of the first computing device. The first persistentstorage may include a spinning disk hard drive, a solid-state drive, orthe like. For example, some embodiments may store the serialized set ofvertices in a solid-state drive of the first computing device. In someembodiments, the first persistent storage may be physically attached toother components of the first computing device or be in communicationwith a processor of the first computing device via a serial or parallelbus.

In some embodiments, the process 2600 may include operations todistribute a set of vertices of a directed graph generated or updated bythe symbolic AI application to a second computing device of thecomputing system, as indicated by block 2612. In some embodiments, thefirst computing device may distribute data from a program state for asmart contract or other symbolic AI application to a second computingdevice. In some embodiments, the data may include a serialized set ofvertices using a first distributed, tamper-evident data store. Forexample, the first computing device may be operated as a node of adecentralized computing platform and may update a first distributed,tamper-evident data store with the set of vertices. The second computingdevice may also be operated as a second node of the decentralizedcomputing platform and obtain the set of vertices from the firstdistributed, tamper-evident data store.

Some embodiments may use one or more consensus algorithms to reach aconsensus regarding a computational result, where the computationalresult may indicate the validity of a blockchain block (or other recordof a decentralized, tamper-evident data store) to communicate, store, ordistribute data. Some embodiments may use one or more consensusalgorithms to verify or reject a result, such as proof of work consensusalgorithm, proof of stake consensus algorithm, proof of burn consensusalgorithm, proof of capacity consensus algorithm, proof of elapsed timeconsensus algorithm, proof of formulation consensus algorithm, or thelike. In some embodiments, different sets of nodes may be assigneddifferent roles to fulfill the function of a consensus algorithm.

In some embodiments, each node of a network may be assigned the samerole with respect to result generation or validation. Alternatively, insome embodiments, a subset of nodes of a network may be selected to havethe authority to perform operations such as generating a blockchainrecord, validating a blockchain record, modifying a program state of anapplication, approving transactions, or the like, where other nodes ofthe network do not have this authority. For example, a subset of nodesmay have the authority to validate a transaction, modify program state,or otherwise administer one or more aspects of smart contract operationsin a decentralized computing platform.

In some embodiments, the selection of the subset of nodes may bepre-determined or otherwise controlled by a central authority. Forexample, nodes of a subset of nodes may be pre-determined during aninitial set-up of a decentralized computing platform or during asubsequent configuration of the decentralized computing platform by anetwork administrator. Alternatively, some embodiments may use aconsensus voting process such as that used for an implementation of theDPoS algorithm across a plurality of computing devices in a network ofcomputing devices. In a consensus voting process, nodes of a network mayvote on a subset of nodes as delegate nodes that will generate andvalidate results, such as results used to generate new blocks of ablockchain. Various voting schemes may be implemented, such as aweighting votes based on stakes provided by each respective node,probabilistically selecting votes based on a score value assigned toeach respective node, or the like.

In some embodiments, operations of the process 2600 may include storinga directed graph based on the set of vertices in a persistent storage ofthe second computing device, as indicated by block 2620. In someembodiments, a persistent storage of the second computing device may bea local memory storage attached to the second computing device, usableby the second computing device, or otherwise accessible to the secondcomputing device via instructions provided by the second computingdevice. For example, if the second computing device is the secondprivate peer node computing device 2515, the persistent storage used tostore the directed graph instance may include the second persistentstorage 2535. Alternatively, or in addition, the persistent storage ofthe second computing device may include a persistent storage of anexternal centralized computing system, such as the external persistentstorage 2542 of the out-of-network computing device 2540. Alternatively,or in addition, the persistent storage of the second computing devicemay include a persistent storage used for a distributed, tamper-evidentdata store. For example, the second computing device may store thedirected graph in a distributed, tamper-evident data store such as anIPFS system, Blockstack system, Swarm system, or the like. By storingdata in a distributed, tamper-evident data store, some embodiments mayincrease the visible security of a history of transactions reflected byor otherwise associated with the directed graph.

In some embodiments, the directed graph or other data based on programstate for an application executing on a decentralized computing platformmay be stored in a record of a data structure such as a relationaldatabase. For example, some embodiments may store a directed graph in adatabase responsive to commands provided in a structured query language(SQL). Alternatively, or in addition, a directed graph or other databased on program state may be stored in a data structure such as anon-relational database (i.e., NoSQL database). For example, someembodiments may store a sequence of values representing a directed graphin a NoSQL database such as MongoDB, Cassandra, Coachbase, HBase, Redis,Hadoop database, Neo4j, or the like. Alternatively, or in addition, thedirected graph or other data based on program state for an applicationmay be stored in other data structures. For example, some embodimentsmay store an instance of the directed graph as a property of an object,value(s) in program state for a second application, a memory image, atree data structure, a JSON document, or the like.

In some embodiments, an instance of the directed graph may be stored inassociation with other values, such as values based on events associatedwith the directed graph. For example, some embodiments may store a setof objects or arrays representing an instance of a directed graph in arecord of a database on a persistent storage. In addition, someembodiments may determine that an event caused a program state changefor an application and resulted in the transition to a new state of adirected graph from a previous state. In response, some embodiments maystore event data associated with the state-updating event in associationwith an instance of the directed graph indicating the new state. Forexample, some embodiments may update a record storing directed graphdata by storing an event message indicating the occurrence of astate-updating event, a timestamp representing the time at which theevent occurred, values parsed from the event message, or the like.

Some embodiments may track the progression of a smart contract directedgraph via a history graph, where the history graph may be represented asa sequence of records or other sets of values useful for generating asequence of versions for a version-control system. Some embodiments mayupdate a history graph by adding a new vertex to the history graph whena change to the smart contract directed graph occurs. For example, someembodiments may determine that a smart contract directed graph has beenchanged based on a comparison with a previous smart contract directedgraph. In response, some embodiments may add a new vertex to a historygraph by adding another entry to a sequence of records corresponding tothe history graph. In some embodiments, the history graph may includedata that is not stored in the smart contract directed graph or mayinclude summarizations of changes to a smart contract directed graphacross an interval of time or a set of changes. Some embodiments maystore a smart contract directed graph by storing an update to the smartcontract directed graph from a previous version of the smart contractdirected graph. Furthermore, some embodiments may store data related toa change in the program state of an application. For example, someembodiments may store a score change associated with an event thatcaused a program state change in an application program state.Alternatively, or in addition, some embodiments may store entityidentifiers of the entities associated with a graph-updating event.

Some embodiments may crawl through a set of blocks of a blockchainstored on a decentralized, tamper-evident data store to track a historyof a directed graph and determine a sequence of previous versions of thedirected. For example, some embodiments may crawl through a sequence ofblocks in a blockchain sequence to determine a sequence of programstates and their associated versions of a directed graph. Someembodiments may use a block crawling operation to track the evolution ofa directed graph through the sequence of blocks from a starting point toa particular time, a particular state of the directed graph, a terminalprogram state, or the like. As discussed elsewhere in this disclosure,the program state history of an application or the history of acorresponding directed graph may be used as training inputs to determineprediction model parameters. These prediction model parameters may thenbe used by a prediction model to predict future entity behavior.Furthermore, some embodiments may use a block-crawling operation toprovide a means of using one or more operations of this disclosure afteran application had already started executing on a decentralized,tamper-evident data store.

In some embodiments, one or more natural language documents may bestored in the second persistent storage in association with the directedgraph. A natural language document may include a text document written ahuman-interpretable language such as English, Spanish, French, MandarinChinese, Japanese, Korean, or the like. For example, a smart contractapplication may be associated with a natural language contract usable toexplain terminology, contract operations, or the like. Some embodimentsmay store a text file of the natural language document in a first recordof a database stored on the second persistent storage, where the firstrecord may also store an instance of the directed graph.

In some embodiments, one or more prediction results using one or moreoperations described in this disclosure may be stored in associationwith a directed graph in persistent storage. For example, someembodiments may use a machine learning system to determine a predicteddirected graph based on a current directed graph and a set of predictedfuture environmental parameters. The predicted directed graph or anotherprediction result (e.g., a cumulative score change) may be stored in apersistent storage in association with the directed graph. For example,a predicted directed graph may be stored in the same database record asa first directed graph, where the first directed graph was used as aninput to generate the predicted directed graph.

In some embodiments, operations of the process 2600 may includereceiving a message at an API of the symbolic A application, asindicated by block 2624. In some embodiments, the message may be sent byan entity in the set of entities associated with a smart contract orother symbolic AI application. For example, a message sent to a smartcontract API may include a query that includes a hashed passkey of afirst entity listed as an entity participating in the smart contract. Asfurther discussed below, different entities may be provided withdifferent permissions that allow the respective entity to accessdifferent amounts of data or be provided with different types of resultsbased on the data.

In some embodiments, the message may encode a graph portion template asa part of a query. In some embodiments, the graph portion template mayrepresent elements of a graph or labels associated with the graph andmay be encoded as a set of strings, characters, numbers, or the like.For example, the message may encode the graph portion template in thetwo of arrays “[R, O, P]” and “[[0,1],[02]],” where the first arrayindicates a set of category labels from a set of mutually exclusivecategory labels for a corresponding set of three vertex templates andthe second array indicates a set of edge templates. In some embodiments,the use of category labels selected from a set of mutually exclusivecategory labels may increase the efficiency of determining matchesbetween a graph portion of a directed graph and a graph portion templateand reduce the possibility of a mismatch between a graph portion and agraph portion template.

A graph portion template may be matched with a portion of a directedgraph, where a graph portion template may be matched with an entiregraph or be matched with a portion of the graph that has fewer verticesor edges than the entire graph. For example, a vertex template mayinclude a vertex that can be matched to a target vertex sharing acategory label with the vertex template if the neighboring vertices ofthe vertex template also share labels with the neighboring vertices ofthe target vertex. Similarly, an edge template may include an edge thatindicates a connection directed from a first vertex template to a secondvertex template and may be matched to an edge of the directed graphbased on their corresponding vertex templates and edge templatedirection matching with the vertices an edge direction of the directedgraph. For example, the graph portion template in the two of arrays “[R,O, P]” and “[[0,1],[02]]” may the matched to a portion of the directedgraph based on the portion including a first, second, and third vertexlabeled as a “rights” norm, an “obligation” norm, and a “prohibition”norm, respectively.

In some embodiments, a message may encode a graph portion template byincluding an index value that references the graph portion templatestored in a library of graph portion templates. After receiving amessage that includes an index value referencing a graph portiontemplate, some embodiments may search the library of graph portiontemplates to determine the graph portion template. For example, amessage may include a first identifier “x0215,” where “x0215” may be anindex value in a library of graph portion templates that is associatedwith a first graph portion template. After receiving the message, someembodiments may search through the library of graph portion templatesfor the first identifier “x0215” retrieve the first graph portiontemplate.

In some embodiments, the message may encode a graph portion templatebased on information included in the message that can be converted intoa graph portion template. Some embodiments may determine a graph portiontemplate from the set of instructions using a rule-based system or a NLPsystem, such as the probabilistic identification system described inprovisional patent application 63/034,255 titled “Semantic ContractMaps.” For example, some embodiments may receive a message that includesthe string “rights norms activated by failed obligations” and convertthe string into a set of arrays representing a graph portion templatesuch as “[“O”, “R”] and “[[0,1]]” by parsing the string, isolating termsassociated with vertices or category labels of vertices, and isolatingterms associated with directed relationships between the vertices.

In some embodiments, a message may encode a category label to determinea result based on vertices having the category label. For example, someembodiments may receive a message that includes the character “R” or theword “right” represent a category label associated with “rights” normvertices. As discussed further below, some embodiments may thendetermine a subset of vertices in the directed graph comprising acategory label. By searching through a directed graph representing asymbolic AI application using a category label encoded in a message,some embodiments may provide the vertices associated with that categorylabel or scores associated with those vertices. This information maythen be used to compute a sum, count, or other summarizing result andinclude the result in a response value.

In some embodiments, the process 2600 may include determining a subsetof vertices based on the message, as indicated by block 2632. A messagemay include a set of instructions or parameters to determine a subset ofvertices of a directed graph. For example, the message may include agraph portion template to determine a subset of vertices. For example,some embodiments may search through a directed graph for the presence ofa graph portion that matches a graph portion template encoded in themessage. In some embodiments, the subset of vertices may include all ofthe vertices that matched with the graph portion template. For example,if a graph portion template includes three vertex templates, and if agraph portion includes three vertices that matched with the three vertextemplates, some embodiments may select each of the three vertices forinclusion in a subset of vertices. Alternatively, some embodiments mayselect only one the subset of vertices that matched with a graph portiontemplate, such as the earliest-activated vertex of the subset ofvertices, the last-triggered vertex of the subset of vertices, thelast-activated vertex of the subset of vertices, or the like.

Alternatively, or in addition, some embodiments may search through adirected graph based on the presence of a set of vertices that includeor is otherwise associated with a category label or set of categorylabels. For example, after receiving a message indicating the categorylabels “rights” and “triggerable,” some embodiments search through adirected graph for all vertices having a “rights” label and“triggerable” label. As discussed further below, this information may beused to determine a summation, a list of active norm vertices, or thelike.

Some embodiments may receive a message that does not limit a search to asingle instance of an application and may involve searching through aplurality of directed graphs representing a plurality of applications.In response, some embodiments may select a plurality of directed graphs,such as a plurality of directed graphs stored in a persistent storage ofthe second computing device, and search through each respective directedgraph to determine a respective subset of vertices. For example, amessage may include instructions to determine a response value for eachsmart contract that an entity is a participating entity. In response toreceiving the message, some embodiments may select a plurality ofdirected graphs based on graph criteria encoded in the message. In someembodiments, the criteria may be explicitly encoded, such as criterialimiting the number of directed graphs to those associated withapplications listing an entity. Each respective directed graph mayrepresent a program state of a different instance of a smart contractapplication, and some embodiments may select a respective subset ofvertices for each of the respective directed graphs of the plurality ofdirected graphs.

In some embodiments, the process 2600 may include determining a subsetof entities of the symbolic AI program based on the subset of vertices,as indicated by block 2636. As discussed in this disclosure, a symbolicAI program may include a set of entities associated with a directedgraph of the symbolic AI program, where one or more entities of the setof entities may trigger one or more conditional statements of the normvertices of the directed graph. For example, a first entity may triggera first vertex by satisfying a conditional statement of the firstvertex, which may activate a second vertex by setting the second vertexto switch from an un-triggerable state to a triggerable state. Someembodiments may determine the subset of entities based on the entitieslisted as capable of triggering vertex or being affected by an eventsatisfying the conditional statement of the norm vertex. For example, avertex may be associated with a first entity and the second entity basedon a conditional statement that is triggered when the first entity sendsa specified hash value to the second entity.

In some embodiments, a first vertex may be associated with a firstentity even if the first vertex itself is not triggered by an eventcaused by the first entity or causes a transaction to occur involvingthe first entity. For example, a first vertex may be associated with afirst entity if the triggering of the first vertex activates a secondvertex that may be triggered by an event caused by the first entity orcauses a transaction involving the first entity. In some embodiments,associating entities with vertices based on neighboring vertices mayincrease the data privacy for each entity listed in a symbolic Aapplication when determining data access permissions. Additionally, oralternatively, associating entities with vertices based on neighboringvertices may increase data accuracy when determining response values forqueries having one or more criteria based on entities.

Some embodiments may associate vertices from the subset of vertices withtheir corresponding set of entities in a persistent storage, such as ina data structure stored in a persistent storage of the second computingdevice. For example, some embodiments may generate a data table withentity identifiers as indices and vertex identifiers as valuescorresponding to those entity identifier indices. Alternatively, or inaddition, some embodiments may generate a data table with vertexidentifiers as indices and entity identifiers as values corresponding tothose vertex identifier indices. As further discussed in thisdisclosure, some embodiments may use these associations to determinewhether or how much data to provide in a response to a message.

In some embodiments, the process 2600 may include determining whether amessage satisfies a set of data retrieval criteria associated with thesubset of entities, as indicated by block 2640. Various entities mayinclude or otherwise be associated with specific criteria indicatingwhat other entities are capable of accessing information about theentity, or what types of information can be disclosed. For example, afirst entity may include criteria that “level I” entities may accessinformation about specific amounts associated with a transaction. Thefirst entity may also include criteria that only “level II entities” mayaccess information regarding whether a specified set of conditionalstatements associated with the obligation norms were satisfied by thefirst entity. Some embodiments may use signature values, entityidentifiers, or other information encoded in a message or otherwiseassociated with the message to determine whether the source of themessage is authorized to access information associated with an entity.

In some embodiments, a message may satisfy the set of data retrievalcriteria even if it fails a criterion of the set of data retrievalcriteria. For example, a set of data retrieval criteria may include afirst criterion that specifies that all entities of a first type beprovided a score range indicating the numeric range of a resourcetransfer that occurred during a previous transaction. The set of dataretrieval criteria may also include a second criterion that specifiesthat all entities of a second type be provided a score indicating theexact amount of the resource transfer that occurred during the previoustransaction. Some embodiments may satisfy the set of data retrievalcriteria by failing the second criterion but satisfying the firstcriterion.

In some embodiments, a message that satisfies the set of data retrievalcriteria may include or otherwise be associated with an identifier of afirst entity, where the first entity is listed as a permitted entity toaccess requested data from the subset of entities. For example, amessage may include an identifier of a first entity having an entitytype of “regulation observer,” and a data retrieval criterion of asecond entity may include the condition that a transaction score of thesecond entity may only be obtained by entities having the entity type“regulation observer.” In response to receiving the message, someembodiments may determine that the message satisfies the data retrievalcriterion of the second entity. Operations of the process 2600 mayproceed to operations described for block 2644 in response to thesatisfaction of the set of data retrieval criteria. Otherwise,operations of the process 2600 may proceed to block 2650.

In some embodiments, the process 2600 may include determining a responsevalue based on the subset of vertices, as indicated by block 2644. Theresponse value may be determined based on instructions or parametersencoded in the message used to determine the subset of vertices. Forexample, some embodiments may receive a message requesting a count ofvertices associated with a certain label. Alternatively, or in addition,some embodiments may receive a message requesting a sum of valuesassociated with the subset of vertices. Alternatively, or in addition,some embodiments may receive a message encoding a function to apply tovalues associated with the subset of vertices.

In some embodiments, the response value may be determined as acombination of scores corresponding to conditional statements associatedwith the subset of vertices. For example, some embodiments may determinea set of outstanding obligation conditions that a first entity mustsatisfy. Some embodiments may parse the conditional statementsassociated with each obligation norm vertex of a subset of vertices todetermine a score change required to satisfy each obligation norm vertexand compute the sum of the scores. Similar operations may be performedto determine sums of scores associated with vertex outcomes, such as thesum of a set of scores associated with rights norm vertices. While theabove describes computations based on sums of scores, some embodimentsmay compute other response values based on a set of scores, such as ameasure of central tendency of a set of scores, a measure of variance ofa set of scores, a maximum value of a set of scores, a minimum value ofa set of scores, or the like.

Alternatively, or in addition, some embodiments may determine a responsevalue based on events triggering the subset of vertices. Someembodiments may determine an event or a history of events correspondingto the activation or triggering of a set of vertices and use the eventor history of events to determine a response value. For example, someembodiments may determine that a sequence of two events including afirst event indicating the allocation of a first amount of memory from afirst entity to a second entity and a second event indicating theallocation of a second amount of memory from the first entity to thesecond entity resulted in the triggering of a subset of vertices. Someembodiments may then compute a mean average between the two amounts ofallocated memory and include the mean average in a response value.Alternatively, or in addition, some embodiments may determine a count ofoccurrence as a response value. For example, some embodiments maydetermine a total number of times that a graph portion matches a graphportion template across five directed graphs representing fiveconcurrently operating smart contracts and include the total number oftimes in a response value. Alternatively, or in addition, someembodiments may include data associated with the vertices or eventscausing the activation or triggering of the vertices in a responsevalue, where. For example, some embodiments may include timestamp data,location data, and a total elapsed time between the earliest activationof a vertex in a subset of vertices and the latest triggering of avertex in the subset of vertices in a response value.

In some embodiments, a message may include instructions to update arecord, a directed graph, a label, or other value stored in a persistentstorage of the second computing device. For example, a message mayinclude text formatted as a SQL command, such as “UPDATE graph_table SETcolumn1=value1 WHERE EntityID=0035,” which may cause some embodiments toupdate a database storing the directed graph or data associated with thedirected graph by setting the “column1” values of records having anentityID of “0035” to be equal to “value1.” While a SQL format is usedas a part of the message, update instructions in various other formatsmay be included in a message to cause some embodiments to update datastored in the persistent storage. For example, a message may include aset of instructions in the form of a set of values, where someembodiments may parse the set of values into parameters for thoseinstructions.

Some embodiments may determine an update confirmation value afterupdating a directed graph or another value associated with anapplication in the persistent storage. For example, some embodiments maygenerate a hash value based on the sets of vertices and edges of thedirected graph or use as a first update confirmation value. Someembodiments may determine whether a second update confirmation value isreceived and satisfies a set of storage update criteria based on thefirst update confirmation value, where the satisfaction of the set ofstorage update criteria may include determining that the first andsecond update confirmation values are equal to each other. For example,some embodiments may receive a second update confirmation value equal to“4325” and compare this second update confirmation value to a firstupdate confirmation value (which may be “4325” or may be a differentvalue) to determine whether the second update confirmation valuesatisfies the first update confirmation value.

In some embodiments, the set of storage update criteria may use anelapsed time threshold within which an update confirmation value must besatisfied in order for an update to the persistent storage to beconsidered to be valid. For example, a message may cause a secondcomputing device to update an instance of the directed graph to includean additional vertex. In response, the second computing device maydetermine a first update confirmation value based on the updateddirected graph. The second computing device may use a set of storageupdate criteria that includes instructions for the second computing towait 30 minutes before undoing the update unless the second computingdevice receives a second update confirmation value based on an update toprogram state for an application operating on a decentralized computingplatform storing a serialized version of the directed graph. By updatinga record in a persistent storage before a program state upon which therecord is based is distributed across a network, some embodiments mayreduce slowdowns caused by relatively slow data distribution andretrieval operations in a decentralized, tamper-evident data store. Forexample, an update to a database used to store a history of programstate that caused by the message may require less than one minute tofinish, where the update may reflect a program state change that wouldrequire 15 minutes to propagate through the nodes of a network operatinga decentralized, tamper-evident data store and reach the persistentmemory.

In some embodiments, operations of the process 2600 may includedetermining whether the message includes instructions to update programstate for the symbolic AI application, as indicated by block 2650. Insome embodiments, the message may include instructions to update programstate of the application. For example, a message may includeinstructions to update a program state of an application executing on adecentralized computing platform by including instructions to set thestatus of a norm vertex labeled as an “obligation” as failed. If themessage includes instructions to update program state of theapplication, operations of the process 2600 may proceed to block 2654.Otherwise, operations of the process 2600 may proceed to block 2660.

In some embodiments, operations of the process 2600 may include updatinga directed graph stored in the second persistent storage of the secondcomputing device, as indicated by block 2654. Updating the directedgraph stored in the persistent storage may include changing an existingrecord stored in the persistent storage of the second computing device.For example, some embodiments may update a record storing an instance ofa directed graph to replace an existing set of indices representing thevertices of a directed graph with a new set of indices representing thevertices of the directed graph. Alternatively, updating the directedgraph stored in the persistent storage may include adding a new entry toa data structure stored in the persistent storage. For example, someembodiments may add a new record to a data structure that includes a newset of values representing a directed graph encoded in the updateparameters of a message received by a hybrid computing system.

In some embodiments, operations of the process 2600 may includeserializing and distributing the directed graph or other updates to thefirst computing device of the computing system, as indicated by block2658. After updating a directed graph in the persistent storage of thesecond computing device, some embodiments may then use the distributionmechanisms of an application executing on a decentralized computingplatform to update other computing devices used to execute theapplication. For example, some embodiments may serialize, using one ormore serialization operations described above, the version of thedirected graph updated by the message at a second computing deviceacting as a node of a node network and distribute the serialized versionto other nodes of the node network from the second computing device.

In some embodiments, operations of the process 2600 may include sendingthe response value to a response destination, as indicated by block2660. The response value may be sent from a computing device capable ofaccessing the second persistent memory storing the subsets of vertices.For example, a query response that includes the response value may besent from the second private peer node computing device 2515 or anothercomputing device of the hybrid network 2510. In some embodiments, themessage may indicate that a known entity having a known destination portor address is the source of a message. For example, some embodiments mayreceive a message indicating that an entity identified as “x345” is thesource of the message, where a list of entities and their associatedproperties may include a response destination for the entity identifiedas “x345,” where the response destination may include an email address,a identifier for a profile of an online messaging service, a phonenumber, a identifier for an API, or the like. Alternatively, or inaddition, the message may encode an explicit response destination towhich the response value is sent. For example, the message may include aresponse destination in the form of an API address that causes someembodiments to send the response value to the API address.

In block diagrams, illustrated components are depicted as discretefunctional blocks, but embodiments are not limited to systems in whichthe functionality described herein is organized as illustrated. Thefunctionality provided by each of the components may be provided bysoftware or hardware modules that are differently organized than ispresently depicted, for example such software or hardware may beintermingled, conjoined, replicated, broken up, distributed (e.g. withina data center or geographically), or otherwise differently organized.The functionality described herein may be provided by one or moreprocessors of one or more computers executing code stored on a tangible,non-transitory, machine readable medium. In some cases, notwithstandinguse of the singular term “medium,” the instructions may be distributedon different storage devices associated with different computingdevices, for instance, with each computing device having a differentsubset of the instructions, an implementation consistent with usage ofthe singular term “medium” herein. In some cases, third party contentdelivery networks may host some or all of the information conveyed overnetworks, in which case, to the extent information (e.g., content) issaid to be supplied or otherwise provided, the information may providedby sending instructions to retrieve that information from a contentdelivery network.

The reader should appreciate that the present application describesseveral independently useful techniques. Rather than separating thosetechniques into multiple isolated patent applications, applicants havegrouped these techniques into a single document because their relatedsubject matter lends itself to economies in the application process. Butthe distinct advantages and aspects of such techniques should not beconflated. In some cases, embodiments address all of the deficienciesnoted herein, but it should be understood that the techniques areindependently useful, and some embodiments address only a subset of suchproblems or offer other, unmentioned benefits that will be apparent tothose of skill in the art reviewing the present disclosure. Due to costsconstraints, some techniques disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionsections of the present document should be taken as containing acomprehensive listing of all such techniques or all aspects of suchtechniques.

It should be understood that the description and the drawings are notintended to limit the present techniques to the particular formdisclosed, but to the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the present techniques as defined by the appended claims.Further modifications and alternative embodiments of various aspects ofthe techniques will be apparent to those skilled in the art in view ofthis description. Accordingly, this description and the drawings are tobe construed as illustrative only and are for the purpose of teachingthose skilled in the art the general manner of carrying out the presenttechniques. It is to be understood that the forms of the presenttechniques shown and described herein are to be taken as examples ofembodiments. Elements and materials may be substituted for thoseillustrated and described herein, parts and processes may be reversed oromitted, and certain features of the present techniques may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the present techniques.Changes may be made in the elements described herein without departingfrom the spirit and scope of the present techniques as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Similarly, reference to “a computer system”performing step A and “the computer system” performing step B caninclude the same computing device within the computer system performingboth steps or different computing devices within the computer systemperforming steps A and B. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X'editems,” used for purposes of making claims more readable rather thanspecifying sequence. Statements referring to “at least Z of A, B, andC,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Zof the listed categories (A, B, and C) and do not require at least Zunits in each category. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.Features described with reference to geometric constructs, like“parallel,” “perpendicular/orthogonal,” “square”, “cylindrical,” and thelike, should be construed as encompassing items that substantiallyembody the properties of the geometric construct, e.g., reference to“parallel” surfaces encompasses substantially parallel surfaces. Thepermitted range of deviation from Platonic ideals of these geometricconstructs is to be determined with reference to ranges in thespecification, and where such ranges are not stated, with reference toindustry norms in the field of use, and where such ranges are notdefined, with reference to industry norms in the field of manufacturingof the designated feature, and where such ranges are not defined,features substantially embodying a geometric construct should beconstrued to include those features within 15% of the definingattributes of that geometric construct. The term “set” may indicate asingle item or a plurality of items, e.g., “set of widgets” may indicateonly one widget or may indicate multiple widgets. The terms “first”,“second”, “third,” “given” and so on, if used in the claims, are used todistinguish or otherwise identify, and not to show a sequential ornumerical limitation. As is the case in ordinary usage in the field,data structures and formats described with reference to uses salient toa human need not be presented in a human-intelligible format toconstitute the described data structure or format, e.g., text need notbe rendered or even encoded in Unicode or ASCII to constitute text;images, maps, and data-visualizations need not be displayed or decodedto constitute images, maps, and data-visualizations, respectively;speech, music, and other audio need not be emitted through a speaker ordecoded to constitute speech, music, or other audio, respectively.Computer implemented instructions, commands, and the like are notlimited to executable code and can be implemented in the form of datathat causes functionality to be invoked, e.g., in the form of argumentsof a function or API call.

In this patent, to the extent any U.S. patents, U.S. patentapplications, or other materials (e.g., articles) have been incorporatedby reference, the text of such materials is only incorporated byreference to the extent that no conflict exists between such materialand the statements and drawings set forth herein. In the event of suchconflict, the text of the present document governs, and terms in thisdocument should not be given a narrower reading in virtue of the way inwhich those terms are used in other materials incorporated by reference.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A tangible, non-transitory, machine-readable medium storinginstructions that when executed by one or more processors effectuateoperations comprising: obtaining, with a computer system, a set ofconditional statements, wherein: a conditional statement of the set ofconditional statements is associated with an outcome subroutine thatspecifies operations in each of one or more branches of the conditionalstatement, a set of index values index the set of conditionalstatements, and a first outcome subroutine of a first conditionalstatement of the set of conditional statements uses a first index valueof the set of index values, wherein the first index value is associatedwith a second conditional statement of the set of conditionalstatements; executing, with the computer system, a program instance ofan application based on the set of conditional statements, whereinprogram state data of the program instance comprises: a set of verticesand a set of directed graph edges, wherein each of the set of verticescomprises a identifier value and is associated with one of the set ofconditional statements, and wherein each of the set of directed graphedges associates a pair of the set of vertices and a direction from atail vertex of the pair to a head vertex of the pair, a set of statuses,wherein each of the set of statuses is associated with one of the set ofvertices, a set of vertex categories, wherein each of the set of vertexcategories is a category value and is associated with a respectivevertex of the set of vertices and is determined based a respectiveconditional statement of the respective vertex, and a set of scores,wherein each respective score of the set of scores is associated with arespective vertex and is based a respective conditional statement of therespective vertex; updating, with the computer system, the program statedata based on a set of inputs comprising a first input, wherein updatingthe program state data comprises: modifying a status of a first vertexof the set of vertices based on the first input, updating a vertexadjacent to the first vertex; and determining, with the computer system,an outcome score based on the set of scores after updating the programstate data.2. The medium of embodiment 1, wherein the status is a first status, andwherein updating the program state data comprises updating the programstate data based on the first status, and wherein the operations furthercomprise: modifying a second status of a second vertex of the set ofvertices based on a second input; updating a third vertex adjacent tothe second vertex, wherein determining the outcome score comprisesdetermining the outcome score after updating the third vertex.3. The medium of embodiment 2, wherein the operations further comprisedetermining the first input based on a probability value associated withone of the set of vertex categories.4. The medium of any of embodiments 2 to 3, wherein the outcome score isa first outcome score, and wherein the program state data is in a firststate before modifying the program state data, and wherein theoperations further comprise: updating a neural network parameter afterupdating the third vertex based on the first outcome score, wherein theneural network parameter comprises a set of probability values assignedto each of a subset of vertices of the set of vertices; determining athird input based on the neural network parameter; updating the programstate data that is in the first state based on the third input; anddetermining a second outcome score after updating the program state databased on the third input.5. The medium of any of embodiments 1 to 4, wherein executing theprogram instance comprises executing the program instance during a firstiteration, and wherein the set of inputs is a first set of inputs, andwherein the outcome score is a first outcome score, and wherein theprogram state data is in a first state before modifying the programstate data, and wherein the operations further comprise: executing theprogram instance during a second iteration by updating the program statedata based on a second set of inputs, wherein the program state data isin the first state before updating the program state data based on thesecond set of inputs; determining a second outcome score based on thesecond set of inputs; and determining a multi-iteration score based onthe first outcome score and the second outcome score.6. The medium of embodiment 5, wherein the operations further comprise:acquiring a third score; and determining a possible event based thethird score using a probability distribution, wherein the probabilitydistribution is based on the multi-iteration score.7. The medium of embodiment 6, wherein determining the possible event iscomprises using a neural network that is trained using inputs based onthe first outcome score and the second outcome score, and wherein theneural network is trained using a training output based on the first setof inputs and the second set of inputs.8. The medium of any of embodiments 5 to 7, wherein: the first set ofinputs is associated with a first weighting value; the second set ofinputs is associated with a second weighting value; and determining themulti-iteration score is based on the first weighting value and thesecond weighting value.9. The medium of any of embodiments 5 to 8, further comprisingdetermining a probability distribution function based on themulti-iteration score.10. The medium of any of embodiments 1 to 9, wherein modifying thestatus of the first vertex comprises determining a set of events,wherein each of the set of events satisfies a condition of the set ofconditional statements.11. The medium of any of embodiments 1 to 10, wherein acquiring the setof conditional statements comprises: acquiring an event; for arespective self-executing protocol of a plurality of self-executingprotocols, determining whether the event satisfies a conditionassociated with the respective self-executing protocol; and acquiringthe set of conditional statements associated with the respectiveself-executing protocol in response to the event satisfying thecondition associated with the respective self-executing protocol.12. The medium of any of embodiments 1 to 11, wherein acquiring the setof conditional statements comprises: acquiring an entity identifier; fora respective self-executing protocol of a plurality of self-executingprotocols, determining whether the entity identifier is in a respectiveset of entities associated with the respective self-executing protocol;and acquiring the set of conditional statements associated with therespective self-executing protocol in response to the entity identifierbeing in the respective set of entities associated with the respectiveself-executing protocol.13. The medium of any of embodiments 1 to 12, the operations furthercomprising: acquiring a first entity identifier and a second entityidentifier; selecting a first set of self-executing protocols from aplurality of self-executing protocols, wherein each of the first set ofself-executing protocols comprises a first set of entities thatcomprises the first entity identifier; determining a second set ofself-executing protocols from the plurality of self-executing protocols,wherein each of the second set of self-executing protocols comprises asecond set of entities that comprises the second entity identifier; anddetermining a set of intermediary entities, wherein each of the set ofintermediary entities is in a set of entities of the first set ofself-executing protocols, and wherein each of the set of intermediaryentities is in a set of entities of the second set of self-executingprotocols.14. The medium of any of embodiments 1 to 13, wherein modifying thestatus of the first vertex comprises setting a first status to indicatethat a first entity fails to transfer a score to a second entity.15. The medium of any of embodiments 1 to 14, the operations furthercomprising: detecting a pattern based on a plurality of the set ofvertices and a plurality of the set of directed graph edges; and sendinga message indicating that the pattern is detected.16. The medium of any of embodiments 1 to 15, the operations furthercomprising determining a measure of central tendency based on theoutcome score.17. The medium of any of embodiments 1 to 16, the operations furthercomprising determining a kurtosis value based on the outcome score,wherein the kurtosis value correlates with a ratio of a first value anda second value, wherein the first value is based on a measure of centraltendency, and wherein the second value is based on a measure ofdispersion.18. The medium of any of embodiments 1 to 17, the operations furthercomprising: acquiring an event message via an application protocolinterface; determining a first set of events based on the event message,wherein the set of inputs does not include the first set of events; andupdating the program state data based on the first set of events,wherein the program state data is updated based on the set of inputsafter the program state data is updated with the first set of events.19. The medium of any of embodiments 1 to 18, further comprising:modifying a first status of a first vertex of the set of vertices toindicate that the first vertex is triggered; modifying a second statusof a second vertex of the set of vertices to indicate that the secondvertex is triggered; and in response to the first status and the secondstatus being modified to indicate they are triggered, triggering a thirdvertex that is adjacent to the first vertex and the second vertex.20. A method comprising: acquiring a set of conditional statements,wherein: a conditional statement of the set of conditional statements isassociated with an outcome subroutine and an index value of a set ofindex values, and a first outcome subroutine of a first conditionalstatement of the set of conditional statements uses a first index valueof the set of index values, wherein the first index value is associatedwith a second conditional statement of the set of conditionalstatements; executing a program instance of an application based on theset of conditional statements, wherein program state data of the programinstance comprises: a set of vertices and a set of directed graph edges,wherein each of the set of vertices comprises a identifier value and isassociated with one of the set of conditional statements, and whereineach of the set of directed graph edges associates a pair of the set ofvertices and a direction from a tail vertex of the pair to a head vertexof the pair, a set of statuses, wherein each of the set of statuses isassociated with one of the set of vertices, and a set of vertexcategories, wherein each of the set of vertex categories is a categoryvalue and is associated with a respective vertex of the set of verticesand is determined based a respective conditional statement of therespective vertex, a set of scores, wherein each respective score of theset of scores is associated with a respective vertex and is based arespective conditional statement of the respective vertex; updating theprogram state data based on a set of inputs comprising a first input,wherein updating the program state data comprises: modifying a status ofa first vertex of the set of vertices based on the first input, updatinga vertex adjacent to the first vertex; and determining an outcome scorebased on the set of scores after updating the program state data.21. A tangible, non-transitory, machine-readable medium storinginstructions that when executed by one or more processors effectuateoperations comprising: obtaining, with one or more processors,identifiers of a plurality of entities; obtaining, with one or moreprocessors, a plurality of symbolic artificial intelligence (AI) models,wherein: each of the plurality of symbolic AI models is configured toproduce outputs responsive to inputs based on events caused by at leastone of the plurality of entities, at least some of the plurality ofentities are associated with outputs of respective symbolic AI models,and at least some of the plurality of entities have respective scorescorresponding to the respective outputs of the symbolic AI models;obtaining, with one or more processors, a plurality of scenarios,wherein: each scenario comprises simulated inputs corresponding to oneor more simulated events, and at least some scenarios comprise aplurality of simulated inputs; determining, with one or more processors,a population of scores of a given entity among the plurality ofentities, wherein respective members of the population of scorescorrespond to respective outputs of the plurality of symbolic AI models,and wherein the respective outputs correspond to respective scenariosamong the plurality of scenarios; and storing, with one or moreprocessors, the population of scores in memory.22. The medium of embodiment 21, wherein at least one of the pluralityof symbolic AI models comprises: a set of vertices and a set of directedgraph edges, wherein each of the set of vertices comprises a identifiervalue and is associated with one of a set of conditional statements, andwherein each of the set of directed graph edges associates a pair of theset of vertices and a direction from a tail vertex of the pair to a headvertex of the pair; a set of statuses, wherein each of the set ofstatuses is associated with one of the set of vertices; a set of vertexcategories, wherein each of the set of vertex categories is a categoryvalue and is associated with a respective vertex of the set of verticesand is determined based a respective conditional statement of therespective vertex; and a set of scores, wherein each respective score ofthe set of scores is associated with a respective vertex and is based arespective conditional statement of the respective vertex.23. The medium of any of embodiments 21 to 22, wherein obtaining theplurality of scenarios comprises: determining a first simulated inputfor a first model of the plurality of symbolic AI models based on amulti-iteration score associated with the first model, wherein the firstmodel is in a first state before updating the first model based on thefirst simulated input; update the first model based on the firstsimulated input to advance the first model to a second state, whereinthe second state is different from the first state; determine a secondinput, wherein the second input may be selected based on scoresassociated with each of a set of possible states associated with thefirst state;update the first model when it is in the second state based on thesecond input to advance the second model to a third state, wherein thethird state is different from the first state and the second state, andwherein the third state satisfies a terminal state criterion, andwherein a terminal state value is associated with the third state; andupdate the score associated with the first model based on the terminalstate value; and determining a scenario of the plurality of scenariosbased on the score.24. The medium of embodiment 23, wherein determining a first set ofsimulated inputs comprises determining the first set of inputs based ona first term and a second term, wherein the first term is based on acount of simulations executed that started from the first state and thesecond term is based on a score value associated with the third state.25. The medium of any of embodiments 21 to 24, wherein determining thepopulation of scores comprises using a convolutional neural network todetermine a respective score based on values in a respective model ofthe symbolic A models.26. The medium of any of embodiments 21 to 25, the operations furthercomprising: fuzzifying the population of scores to provide a set offuzzified inputs, wherein fuzzifying the outputs comprises using amembership function to determine a degree of membership, and wherein thefuzzified inputs comprises the degree of membership; determine afuzzified outcome score based on the degree of membership using aninference engine, wherein the inference comprises a set of executablerules that may be matched to the fuzzified inputs; and determine a labelassociated with a smart contract based on the fuzzified outcome score.27. The medium of any of embodiments 21 to 26, wherein obtaining theplurality of scenarios comprises: determining a first scenario for afirst symbolic AI model of the plurality of A models based on a firstset of weights corresponding to each of a set of categories, wherein thefirst symbolic AI model comprises a first plurality of the set ofcategories; and determining a second scenario for a second symbolic AImodel of the plurality of AI models based on the first set of weights,wherein the second symbolic AI model comprises a second plurality of theset of categories.28. The medium of any of embodiments 21 to 27, wherein determining thesimulated input comprises using a decision tree, wherein the decisiontree comprises a first tree node and a second tree node, and wherein thefirst tree node is associated with a first score, and wherein the firsttree node is associated with a second score and wherein the operationsfurther comprise: determining whether the first score is greater than asecond score; and in response to the first score being greater than thesecond score, determining the simulated input based on a valueassociated with the first tree node.29. The medium of any of embodiments 21 to 28, further comprisingupdating a set of parameters of a neural network based on the populationof scores, wherein the neural network provides a weighting valueassociated with a decision to cancel a self-executing protocol.30. The medium of embodiment 29, wherein determining the population ofscores of a given entity among the plurality of entities comprisesdetermining a sum of the scores.31. A method to perform any of the operations of embodiments 21 to 30.32. A method to perform any of the operations of embodiments 1 to 19.33. A system, comprising: one or more processors; and memory storinginstructions that when executed by the processors cause the processorsto effectuate operations comprising: the operations of any one ofembodiments 1-19.34. A system, comprising: one or more processors; and memory storinginstructions that when executed by the processors cause the processorsto effectuate operations comprising: the operations of any one ofembodiments 21 to 30.

What is claimed is:
 1. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising: obtaining, with a computer system, a first directed graph of a first program state of a symbolic artificial intelligence (AI) model, wherein: the first directed graph comprises a first set of vertices and a set of directed edges, the first directed graph encodes a set of conditions and is associated with a first entity and a second entity, the conditions being conditional statements, each respective vertex of the first set of vertices is associated with a status among a set of types of status, wherein the set of types of status comprises a first status indicating that the respective vertex is satisfied, a second status indicating that the respective vertex is failed, and a third status indicating that the respective vertex is not satisfied but satisfiable, and each respective vertex of the first set of vertices is categorized in a vertex category among a set of vertex categories, the set of vertex categories comprising a first category, and the first set of vertices comprises a first vertex and a second vertex, wherein the first vertex is categorized as being of the first category, and wherein: a directed edge associates with first vertex with the second vertex, and the first vertex is associated with a first condition satisfiable by a first event caused by the first entity, and wherein a change of status of the first vertex from the third status causes the status of the second vertex to be changed to the third status; simulating, with the computer system, evolving program state of the symbolic A model from the first program state by evaluating conditions of the set of conditions of the first directed graph to form a second directed graph, wherein: the second directed graph comprises a second set of vertices, and wherein a third vertex of the second set of vertices is associated with a second condition satisfiable by a second event caused by the second entity, simulating comprises determining a set of action values of the first entity based on the second directed graph by: determining a set of reward values based on a second set of conditions associated with the second directed graph, wherein each of the set of reward values is associated with a vertex of the second directed graph, and determining the set of action values based on the set of reward values and a set of paths starting from the first vertex to a terminal vertex; and determining and storing in memory, with the computer system, an outcome program state based on the set of action values, wherein the outcome program state is different from the first program state.
 2. The medium of claim 1, wherein evolving from the first program state further comprises: determining a set of active vertices of the first set of vertices, wherein each respective active vertex of the set of active vertices may be triggered by an action of the first entity to triggers an adjacent vertex of the respective active vertex; and determining a first set of child vertices, wherein each respective child vertex of the first set of child vertices is adjacent to one respective active vertex of the set of active vertices, wherein the second set of vertices comprises the first set of child vertices.
 3. The medium of claim 1, wherein evolving from the first program state further comprises evolving from the first program state to each of a set of terminal program states over a plurality of simulated state evolutions, wherein each respective simulated state evolution determines a respective path through the second directed graph that ends at a terminal program state, and wherein the plurality of simulated state evolutions provide a plurality of paths such that each terminal vertex of the second directed graph is in at least one of the plurality of paths.
 4. The medium of claim 1, wherein determining the set of action values further comprises: determining an initial set of actions using a trained neural network based on the first directed graph, wherein each respective action of the initial set of actions is performable by the first entity and is associated with a respective score of an initial set of action values determined by the trained neural network; and iteratively traversing the second directed graph based on the initial set of actions and the initial set of action values using a tree search operation to determine the set of action values.
 5. The medium of claim 4, wherein iteratively traversing the second directed graph comprises: determining a first heuristic value based on the first category, wherein the first heuristic value is associated with satisfaction of the first vertex; determining a second heuristic value based on the first category, wherein the second heuristic value is associated with failure of the first vertex; and determining whether the first vertex is satisfied based on the first heuristic value and the second heuristic value, wherein the first heuristic value is associated with a greater probability of selection than the second heuristic value.
 6. The medium of claim 1, wherein the outcome program state is a first predicted outcome program state, the operations further comprise: determining a set of outcome program states comprising the first predicted outcome program state; obtaining an event performed by the second entity, wherein the second event causes the program state to change to an actual outcome program state; determining whether an unexpected event threshold is satisfied based on whether the actual outcome program state is not in the set of outcome program states; and sending a message indicating that the unexpected event threshold is satisfied.
 7. The medium of claim 1, wherein the operations further comprise: determining a transaction graph based on a set of smart contract programs, wherein a first transaction graph vertex of the transaction graph is associated with the first entity, and wherein a second transaction graph vertex of the transaction graph is associated with the second entity; determining a transaction path between the first entity and the second entity; determining an inter-entity score based on the transaction path; and wherein determining the outcome program state comprises determining the outcome program state based on the inter-entity score.
 8. The medium of claim 7, wherein the operations further comprise: determining that the transaction path is a cyclical path, wherein the cyclical path comprises a set of transaction graph edges that are connected and begin and end at a same transaction graph vertex; and storing a value indicating that the transaction path is cyclical.
 9. The medium of claim 7, wherein determining the transaction graph comprises: traversing a set of directed graphs of the set of smart contract programs to determine a set of score changes between a pair of entities of the transaction graph; and updating, for each of the set of score changes between the pair of entities, a transaction graph edge associating the pair of entities.
 10. The medium of claim 7, wherein the operations further comprise: determining a set of smart contract programs associated with the first transaction graph vertex, wherein each smart contract program of the set of smart contract programs is determined to cause a score change for the first entity based on conditions of the set of smart contract programs; and determining a set of contribution weights, wherein each respective contribution weight of the set of contribution weights is associated with a respective smart contract program of the set of smart contract programs, and wherein each respective contribution weight is correlated with a ratio by which the respective smart contract program contributes to a net score change of the first entity.
 11. The medium of claim 7, wherein determining the transaction path between the first entity and the second entity the operations further comprise: obtaining a transaction path threshold; determining whether a set of transaction graph edges of the transaction graph satisfies the transaction path threshold; and responsive to a determination that the set of transaction graph edges satisfies the transaction path threshold, determining the transaction path between the first entity and the second entity based on the set of transaction graph edges.
 12. The medium of claim 1, and wherein satisfying a failure threshold of the vertex causes the vertex to be associated with the second status, and wherein the operations further comprise: determining whether the second entity is associated with a third event caused by the second entity that resulted in a fourth vertex being associated with the second status, wherein the fourth vertex is associated with the first category; and responsive to the second entity being associated with the third event caused by the second entity that resulted in the fourth vertex being associated with the second status, reduce a reward value determined from a score transfer from the second entity to the first entity.
 13. The medium of claim 1, wherein the operations further comprise categorizing one or more quantitative values of the first program state before determining the set of action values.
 14. The medium of claim 1, further comprising determining whether the first vertex should be associated with the second status based on determining whether a time point satisfies a failure time threshold.
 15. The medium of claim 1, wherein determining the set of action values of the first entity comprises using an intelligent agent, wherein the intelligent agent comprises: a set of stored parameters; a first routine to update the set of stored parameters one or more times; a second routine to determine action values based on the set of stored parameters and on the first program state.
 16. The medium of claim 15, wherein using the intelligent agent comprises: determining a first path score associated with a first path from the first vertex to a terminal vertex of the second directed graph; determining a first weight based on reaching the first program state from an initial program state; determining an intermediate program state based on an event that changes a status of the first vertex to the first status or the second status; determining a second weight based on reaching a terminal program state from the intermediate program state; determining a counterfactual regret value based on a summation comprising a product of the first path score, the first weight, and the second weight; and determining the set of action values based on the counterfactual regret value.
 17. The medium of claim 1, wherein determining the set of reward values based on the second set of conditions comprises: determining a threshold value to satisfy the first vertex based on a condition associated with the first vertex; and determining a first reward value based on the threshold value, wherein the first reward value is associated with the first vertex.
 18. The medium of claim 1, wherein obtaining the first directed graph of the first program state further comprises: determining whether the first program state is different from a previous program state; and responsive to a determination that the first program state is different from the previous program state, obtaining the first directed graph of the first program state.
 19. The medium of claim 1, wherein determining the second directed graph comprises: varying a first set of threshold values of the set of conditions to determine a set of modified values; obtaining an initial set of events causable by the first entity based on the set of modified values; determining a set of possible events based on the initial set of events causable by the first entity using a first trained neural network, wherein the first trained neural network is trained using a set of self-play operations; and determining the second directed graph based on the set of possible events.
 20. The medium of claim 1, wherein evolving from the first program state comprises: obtaining a failure penalty; wherein the second directed graph comprises a first path that includes a vertex having the second status; and modifying an action value associated with the first path based on the failure penalty.
 21. The medium of claim 1, wherein the operations further comprise: determining an outcome score associated with the outcome program state; determining whether the outcome score satisfies an outcome score threshold; and responsive to the outcome score satisfying the outcome score threshold, generate an alert indicating that the outcome score threshold is satisfied.
 22. The medium of claim 1, wherein determining the action value further comprises determining an action value based on an entity property associated with the first entity or the second entity.
 23. The medium of claim 1, further comprising determining the set of paths by performing a set of simulated state evolutions, wherein performing each respective simulated state evolution of the set of simulated state evolutions comprises: determining a set of computed values using a random, pseudorandom, or quasi-random number generation method; and selecting a respective subset of vertices of a respective path based on the set of computed values.
 24. The medium of claim 1, wherein simulating program state evolution comprises simulating program state evolution for at least 10,000 iterations from an initial program state to a terminal program state.
 25. The medium of claim 1, wherein a count of vertices in the first directed graph is greater than 1000 vertices.
 26. The medium of claim 1, further comprising a means of determining the outcome program state.
 27. The medium of claim 1, wherein the operations further comprise: determining a transaction graph based on a set of smart contract programs, wherein a first transaction graph vertex of the transaction graph is associated with the first entity, and wherein a second transaction graph vertex of the transaction graph is associated with the second entity; determining a transaction path between the first entity and the second entity; determining an inter-entity score based on the transaction path; and wherein determining the outcome program state comprises determining the outcome program state based on the inter-entity score.
 28. The medium of claim 1, wherein the operations further comprise: determining a plurality of outcome states; and determining a population score based on the plurality of outcome states.
 29. A method comprising: obtaining, with a computer system, a first directed graph of a first program state of a symbolic artificial intelligence (AI) model, wherein: the first directed graph comprises a first set of vertices and a set of directed edges, the first directed graph encodes a set of conditions and is associated with a first entity and a second entity, the conditions being conditional statements, each respective vertex of the first set of vertices is associated with a status among a set of types of status, wherein the set of types of status comprises a first status indicating that the respective vertex is satisfied, a second status indicating that the respective vertex is failed, and a third status indicating that the respective vertex is not satisfied but satisfiable, and each respective vertex of the first set of vertices is categorized in a vertex category among a set of vertex categories, the set of vertex categories comprising a first category, and the first set of vertices comprises a first vertex and a second vertex, wherein the first vertex is categorized as being of the first category, and wherein: a directed edge associates with first vertex with the second vertex, and the first vertex is associated with a first condition satisfiable by a first event caused by the first entity, and wherein a change of status of the first vertex from the third status causes the status of the second vertex to be changed to the third status; simulating, with the computer system, evolving program state of the symbolic A model from the first program state by evaluating conditions of the set of conditions of the first directed graph to form a second directed graph, wherein: the second directed graph comprises a second set of vertices, and wherein a third vertex of the second set of vertices is associated with a second condition satisfiable by a second event caused by the second entity, simulating comprises determining a set of action values of the first entity based on the second directed graph by: determining a set of reward values based on a second set of conditions associated with the second directed graph, wherein each of the set of reward values is associated with a vertex of the second directed graph, and determining the set of action values based on the set of reward values and a set of paths starting from the first vertex to a terminal vertex; and determining and storing in memory, with the computer system, an outcome program state based on the set of action values, wherein the outcome program state is different from the first program state.
 30. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising: obtaining, with one or more processors, a symbolic artificial intelligence (AI) model, wherein the symbolic AI model is configured to produce an outcome state responsive to an input based on events, wherein at least some of the events are caused by a first entity or a second entity; obtaining, with one or more processors, a first scenario and a second scenario, wherein the first scenario causes the failure of a condition associated with a norm of the symbolic AI model, and wherein the second scenario satisfies the condition associated with the norm of the symbolic AI model; obtaining, with one or more processors, a failure penalty value; determining, with one or more processors, a first outcome state based on the symbolic AI model, the first scenario, and the failure penalty value; determining, with one or more processors, a second outcome state based on the symbolic AI model and the second scenario; determining, with one or more processors, an outcome score based on the first outcome state and the second outcome state; and storing, with one or more processors, the outcome score in memory. 